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class="user-summary-cta-container"><div class="user-summary-container"><div class="social-profile-avatar-container"><img class="profile-avatar u-positionAbsolute" alt="Shubhashis Sengupta" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/4646101/1948174/2304095/s200_shubhashis.sengupta.jpg" /></div><div class="title-container"><h1 class="ds2-5-heading-sans-serif-sm">Shubhashis Sengupta</h1><div class="affiliations-container fake-truncate js-profile-affiliations"><div><a class="u-tcGrayDarker" href="https://iimcal.academia.edu/">Indian Institute of Management Calcutta</a>, <a class="u-tcGrayDarker" href="https://iimcal.academia.edu/Departments/Management_Information_Systems/Documents">Management Information Systems</a>, <span class="u-tcGrayDarker">Alumnus</span></div></div></div></div><div class="sidebar-cta-container"><button 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data-broccoli-component="user-info.followees-count" data-click-track="profile-expand-user-info-following"><p class="label">Following</p><p class="data">11</p></div></a><a href="/ShubhashisSengupta/mentions"><div class="stat-container"><p class="label">Mentions</p><p class="data">1</p></div></a><span><div class="stat-container"><p class="label"><span class="js-profile-total-view-text">Public Views</span></p><p class="data"><span class="js-profile-view-count"></span></p></div></span></div><div class="user-bio-container"><div class="profile-bio fake-truncate js-profile-about" style="margin: 0px;"><b>Address: </b>Bangalore, Karnataka, India<br /><div class="js-profile-less-about u-linkUnstyled u-tcGrayDarker u-textDecorationUnderline u-displayNone">less</div></div></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="4646101" href="https://www.academia.edu/Documents/in/Engineering"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://iimcal.academia.edu/ShubhashisSengupta","location":"/ShubhashisSengupta","scheme":"https","host":"iimcal.academia.edu","port":null,"pathname":"/ShubhashisSengupta","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Engineering"]}" data-trace="false" data-dom-id="Pill-react-component-ae3791d2-89bd-451c-8d85-4042042d5581"></div> <div id="Pill-react-component-ae3791d2-89bd-451c-8d85-4042042d5581"></div> </a></div></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Shubhashis Sengupta</h3></div><div class="js-work-strip profile--work_container" data-work-id="106373319"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/106373319/Predicting_Reputation_Score_of_Users_in_Stack_overflow_with_Alternate_Data"><img alt="Research paper thumbnail of Predicting Reputation Score of Users in Stack-overflow with Alternate Data" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/106373319/Predicting_Reputation_Score_of_Users_in_Stack_overflow_with_Alternate_Data">Predicting Reputation Score of Users in Stack-overflow with Alternate Data</a></div><div class="wp-workCard_item"><span>Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="106373319"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa 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wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/101436549/Neural_response_generation_for_task_completion_using_conversational_knowledge_graph">Neural response generation for task completion using conversational knowledge graph</a></div><div class="wp-workCard_item"><span>PLOS ONE</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Effective dialogue generation for task completion is challenging to build. The task requires the ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Effective dialogue generation for task completion is challenging to build. The task requires the response generation system to generate the responses consistent with intent and slot values, have diversity in response and be able to handle multiple domains. The response also needs to be context relevant with respect to the previous utterances in the conversation. In this paper, we build six different models containing Bi-directional Long Short Term Memory (Bi-LSTM) and Bidirectional Encoder Representations from Transformers (BERT) based encoders. To effectively generate the correct slot values, we implement a copy mechanism at the decoder side. To capture the conversation context and the current state of the conversation we introduce a simple heuristic to build a conversational knowledge graph. Using this novel algorithm we are able to capture important aspects in a conversation. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="101436548"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/101436548/Towards_Sentiment_and_Emotion_aided_Intent_Detection"><img alt="Research paper thumbnail of Towards Sentiment and Emotion aided Intent Detection" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/101436548/Towards_Sentiment_and_Emotion_aided_Intent_Detection">Towards Sentiment and Emotion aided Intent Detection</a></div><div class="wp-workCard_item"><span>2022 26th International Conference on Pattern Recognition (ICPR)</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="101436548"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="101436548"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 101436548; 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These virtual assistants, however, assume specific, predictable end-user behavior, such as predefined/servable objectives, which results in conversation failures in challenging situations, such as when goals are unavailable. Methodology Inspired by the practice and its efficacy, we propose an end-to-end framework for task-oriented persuasive dialogue generation that combines pre-training and reinforcement learning for generating context-aware persuasive responses. We utilize four novel rewards to improve consistency and repetitiveness in generated responses. Additionally, a meta-learning strategy has also been utilized to make the model parameters better for domain adaptation. Furthermore, we also curate a personalized persuasive dialogue (PPD) corpus, which contains utterance-level intent, slot, sentiment, and persuasio...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="915583f80b51299045779bc50ef15967" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":101978982,"asset_id":101436544,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/101978982/download_file?st=MTczMjQ1NzQ4Niw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="101436544"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="101436544"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 101436544; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=101436544]").text(description); $(".js-view-count[data-work-id=101436544]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 101436544; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='101436544']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 101436544, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "915583f80b51299045779bc50ef15967" } } $('.js-work-strip[data-work-id=101436544]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":101436544,"title":"Reinforcing personalized persuasion in task-oriented virtual sales assistant","translated_title":"","metadata":{"abstract":"Purpose Existing task-oriented virtual agents can assist users with simple tasks like ticket booking, hotel reservations, etc. effectively and with high confidence. These virtual assistants, however, assume specific, predictable end-user behavior, such as predefined/servable objectives, which results in conversation failures in challenging situations, such as when goals are unavailable. Methodology Inspired by the practice and its efficacy, we propose an end-to-end framework for task-oriented persuasive dialogue generation that combines pre-training and reinforcement learning for generating context-aware persuasive responses. We utilize four novel rewards to improve consistency and repetitiveness in generated responses. Additionally, a meta-learning strategy has also been utilized to make the model parameters better for domain adaptation. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="92051138"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/92051138/Dynamic_integration_of_heterogeneous_enterprise_data_a_grid_based_approach"><img alt="Research paper thumbnail of Dynamic integration of heterogeneous enterprise data – a grid based approach" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/92051138/Dynamic_integration_of_heterogeneous_enterprise_data_a_grid_based_approach">Dynamic integration of heterogeneous enterprise data – a grid based approach</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Business decisions require information to be available at the right time and at the right place. ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Business decisions require information to be available at the right time and at the right place. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88142881"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88142881/From_Opinion_Mining_to_Improvement_Mining_Understanding_Product_Improvements_from_User_Reviews"><img alt="Research paper thumbnail of From Opinion Mining to Improvement Mining : Understanding Product Improvements from User Reviews" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88142881/From_Opinion_Mining_to_Improvement_Mining_Understanding_Product_Improvements_from_User_Reviews">From Opinion Mining to Improvement Mining : Understanding Product Improvements from User Reviews</a></div><div class="wp-workCard_item"><span>Forum for Information Retrieval Evaluation</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A valuable trove of information exists for product(s) or services online via user opinions like d...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A valuable trove of information exists for product(s) or services online via user opinions like detailed reviews provided by customers on popular e-commerce websites. 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In this work, we describe a method to extract possible product / service improvements from opinionated text in the form of non-conflicting negative feedback, user tips, recommendations, product usage details, feature suggestions, and sp...","internal_url":"https://www.academia.edu/88142881/From_Opinion_Mining_to_Improvement_Mining_Understanding_Product_Improvements_from_User_Reviews","translated_internal_url":"","created_at":"2022-10-08T21:00:02.075-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":4646101,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"From_Opinion_Mining_to_Improvement_Mining_Understanding_Product_Improvements_from_User_Reviews","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":4646101,"first_name":"Shubhashis","middle_initials":null,"last_name":"Sengupta","page_name":"ShubhashisSengupta","domain_name":"iimcal","created_at":"2013-06-24T15:07:02.645-07:00","display_name":"Shubhashis Sengupta","url":"https://iimcal.academia.edu/ShubhashisSengupta"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis"}],"urls":[{"id":24596197,"url":"https://doi.org/10.1145/3503162.3503166"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88142878"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88142878/A_persona_aware_persuasive_dialogue_policy_for_dynamic_and_co_operative_goal_setting"><img alt="Research paper thumbnail of A persona aware persuasive dialogue policy for dynamic and co-operative goal setting" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88142878/A_persona_aware_persuasive_dialogue_policy_for_dynamic_and_co_operative_goal_setting">A persona aware persuasive dialogue policy for dynamic and co-operative goal setting</a></div><div class="wp-workCard_item"><span>Expert Systems with Applications</span><span>, 2022</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88142878"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88142878"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88142878; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88142877"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88142877/Combining_exogenous_and_endogenous_signals_with_a_semi_supervised_co_attention_network_for_early_detection_of_COVID_19_fake_tweets"><img alt="Research paper thumbnail of Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets" class="work-thumbnail" src="https://attachments.academia-assets.com/92175971/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88142877/Combining_exogenous_and_endogenous_signals_with_a_semi_supervised_co_attention_network_for_early_detection_of_COVID_19_fake_tweets">Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets</a></div><div class="wp-workCard_item"><span>ArXiv</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Fake tweets are observed to be ever-increasing, demanding immediate countermeasures to combat the...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Fake tweets are observed to be ever-increasing, demanding immediate countermeasures to combat their spread. During COVID-19, tweets with misinformation should be flagged and neutralised in their early stages to mitigate the damages. Most of the existing methods for early detection of fake news assume to have enough propagation information for large labelled tweets – which may not be an ideal setting for cases like COVID-19 where both aspects are largely absent. In this work, we present ENDEMIC, a novel early detection model which leverages exogenous and endogenous signals related to tweets, while learning on limited labelled data. We first develop a novel dataset, called ECTF for early COVID-19 Twitter fake news, with additional behavioural test-sets to validate early detection. We build a heterogeneous graph with follower-followee, user-tweet, and tweet-retweet connections and train a graph embedding model to aggregate propagation information. Graph embeddings and contextual featur...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ac564ae0d8e342c1c34efe01bbf1a972" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92175971,"asset_id":88142877,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92175971/download_file?st=MTczMjQ1NzQ4Niw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88142877"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88142877"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88142877; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=88142877]").text(description); $(".js-view-count[data-work-id=88142877]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 88142877; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='88142877']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 88142877, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ac564ae0d8e342c1c34efe01bbf1a972" } } $('.js-work-strip[data-work-id=88142877]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":88142877,"title":"Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets","translated_title":"","metadata":{"abstract":"Fake tweets are observed to be ever-increasing, demanding immediate countermeasures to combat their spread. During COVID-19, tweets with misinformation should be flagged and neutralised in their early stages to mitigate the damages. Most of the existing methods for early detection of fake news assume to have enough propagation information for large labelled tweets – which may not be an ideal setting for cases like COVID-19 where both aspects are largely absent. In this work, we present ENDEMIC, a novel early detection model which leverages exogenous and endogenous signals related to tweets, while learning on limited labelled data. We first develop a novel dataset, called ECTF for early COVID-19 Twitter fake news, with additional behavioural test-sets to validate early detection. We build a heterogeneous graph with follower-followee, user-tweet, and tweet-retweet connections and train a graph embedding model to aggregate propagation information. Graph embeddings and contextual featur...","publisher":"PAKDD","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"ArXiv"},"translated_abstract":"Fake tweets are observed to be ever-increasing, demanding immediate countermeasures to combat their spread. During COVID-19, tweets with misinformation should be flagged and neutralised in their early stages to mitigate the damages. Most of the existing methods for early detection of fake news assume to have enough propagation information for large labelled tweets – which may not be an ideal setting for cases like COVID-19 where both aspects are largely absent. In this work, we present ENDEMIC, a novel early detection model which leverages exogenous and endogenous signals related to tweets, while learning on limited labelled data. We first develop a novel dataset, called ECTF for early COVID-19 Twitter fake news, with additional behavioural test-sets to validate early detection. We build a heterogeneous graph with follower-followee, user-tweet, and tweet-retweet connections and train a graph embedding model to aggregate propagation information. Graph embeddings and contextual featur...","internal_url":"https://www.academia.edu/88142877/Combining_exogenous_and_endogenous_signals_with_a_semi_supervised_co_attention_network_for_early_detection_of_COVID_19_fake_tweets","translated_internal_url":"","created_at":"2022-10-08T20:59:59.695-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":4646101,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":92175971,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/92175971/thumbnails/1.jpg","file_name":"2104.05321v1.pdf","download_url":"https://www.academia.edu/attachments/92175971/download_file?st=MTczMjQ1NzQ4Niw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Combining_exogenous_and_endogenous_signa.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/92175971/2104.05321v1-libre.pdf?1665291953=\u0026response-content-disposition=attachment%3B+filename%3DCombining_exogenous_and_endogenous_signa.pdf\u0026Expires=1732435682\u0026Signature=PVROXgMfo988nNv-ILmU7~dM8HrHLSUGsxlXa4nL3isp1mNQEiaqie~qqFoG1DRlGaZtqbDnDV4oa4si8HsYakuyQ~uEEyBwRF25S5HcDzSC8Yzx2TAn0n37QsrROVWkNkJP12QkABcmZFuIeqSQAhgr8lAbTOr5Wn9I3Hqu~qqYFrbnhNXP57Ktu39gzeXQQ5BHTbFwpcxPtDvgSsBs89CPNW-f42D2bs2q2WRuVnkSND656kTDzIs4-jhR6aCZrDcSAQ~SzgHOSFpD6TPIAEB5C17Eaw9a8tImXuFgUi8ZhCLSqEhqRxFvXkwuGJJwaW1wPZYj748mxq5fz1Fubg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Combining_exogenous_and_endogenous_signals_with_a_semi_supervised_co_attention_network_for_early_detection_of_COVID_19_fake_tweets","translated_slug":"","page_count":12,"language":"en","content_type":"Work","owner":{"id":4646101,"first_name":"Shubhashis","middle_initials":null,"last_name":"Sengupta","page_name":"ShubhashisSengupta","domain_name":"iimcal","created_at":"2013-06-24T15:07:02.645-07:00","display_name":"Shubhashis Sengupta","url":"https://iimcal.academia.edu/ShubhashisSengupta"},"attachments":[{"id":92175971,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/92175971/thumbnails/1.jpg","file_name":"2104.05321v1.pdf","download_url":"https://www.academia.edu/attachments/92175971/download_file?st=MTczMjQ1NzQ4Niw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Combining_exogenous_and_endogenous_signa.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/92175971/2104.05321v1-libre.pdf?1665291953=\u0026response-content-disposition=attachment%3B+filename%3DCombining_exogenous_and_endogenous_signa.pdf\u0026Expires=1732435682\u0026Signature=PVROXgMfo988nNv-ILmU7~dM8HrHLSUGsxlXa4nL3isp1mNQEiaqie~qqFoG1DRlGaZtqbDnDV4oa4si8HsYakuyQ~uEEyBwRF25S5HcDzSC8Yzx2TAn0n37QsrROVWkNkJP12QkABcmZFuIeqSQAhgr8lAbTOr5Wn9I3Hqu~qqYFrbnhNXP57Ktu39gzeXQQ5BHTbFwpcxPtDvgSsBs89CPNW-f42D2bs2q2WRuVnkSND656kTDzIs4-jhR6aCZrDcSAQ~SzgHOSFpD6TPIAEB5C17Eaw9a8tImXuFgUi8ZhCLSqEhqRxFvXkwuGJJwaW1wPZYj748mxq5fz1Fubg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":266831,"name":"Graph","url":"https://www.academia.edu/Documents/in/Graph"},{"id":276623,"name":"Misinformation","url":"https://www.academia.edu/Documents/in/Misinformation"},{"id":3193313,"name":"arXiv","url":"https://www.academia.edu/Documents/in/arXiv"},{"id":3686510,"name":"Coronavirus Disease 2019 (COVID-19)","url":"https://www.academia.edu/Documents/in/Coronavirus_Disease_2019_COVID-19_"}],"urls":[{"id":24596195,"url":"https://arxiv.org/abs/2104.05321"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88142876"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88142876/A_dynamic_goal_adapted_task_oriented_dialogue_agent"><img alt="Research paper thumbnail of A dynamic goal adapted task oriented dialogue agent" class="work-thumbnail" src="https://attachments.academia-assets.com/92175969/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88142876/A_dynamic_goal_adapted_task_oriented_dialogue_agent">A dynamic goal adapted task oriented dialogue agent</a></div><div class="wp-workCard_item"><span>PLOS ONE</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Purpose Existing virtual agents (VAs) present in dialogue systems are either information retrieva...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Purpose Existing virtual agents (VAs) present in dialogue systems are either information retrieval based or static goal-driven. However, in real-world situations, end-users might not have a known and fixed goal beforehand for the task, i.e., they may upgrade/downgrade/update their goal components in real-time to maximize their utility values. Existing VAs are unable to handle such dynamic goal-oriented situations. Methodology Due to the absence of any related dialogue dataset where such choice deviations are present, we have created a conversational dataset called Deviation adapted Virtual Agent(DevVA), with the manual annotation of its corresponding intents, slots, and sentiment labels. A Dynamic Goal Driven Dialogue Agent (DGDVA) has been developed by incorporating a Dynamic Goal Driven Module (GDM) on top of a deep reinforcement learning based dialogue manager. In the course of a conversation, the user sentiment provides grounded feedback about agent behavior, including goal serv...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="daf8e0fa595f635f275083e340aba86b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92175969,"asset_id":88142876,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92175969/download_file?st=MTczMjQ1NzQ4Niw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88142876"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88142876"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88142876; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=88142876]").text(description); $(".js-view-count[data-work-id=88142876]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 88142876; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='88142876']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 88142876, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "daf8e0fa595f635f275083e340aba86b" } } $('.js-work-strip[data-work-id=88142876]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":88142876,"title":"A dynamic goal adapted task oriented dialogue agent","translated_title":"","metadata":{"abstract":"Purpose Existing virtual agents (VAs) present in dialogue systems are either information retrieval based or static goal-driven. However, in real-world situations, end-users might not have a known and fixed goal beforehand for the task, i.e., they may upgrade/downgrade/update their goal components in real-time to maximize their utility values. Existing VAs are unable to handle such dynamic goal-oriented situations. Methodology Due to the absence of any related dialogue dataset where such choice deviations are present, we have created a conversational dataset called Deviation adapted Virtual Agent(DevVA), with the manual annotation of its corresponding intents, slots, and sentiment labels. A Dynamic Goal Driven Dialogue Agent (DGDVA) has been developed by incorporating a Dynamic Goal Driven Module (GDM) on top of a deep reinforcement learning based dialogue manager. In the course of a conversation, the user sentiment provides grounded feedback about agent behavior, including goal serv...","publisher":"Public Library of Science (PLoS)","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"PLOS ONE"},"translated_abstract":"Purpose Existing virtual agents (VAs) present in dialogue systems are either information retrieval based or static goal-driven. However, in real-world situations, end-users might not have a known and fixed goal beforehand for the task, i.e., they may upgrade/downgrade/update their goal components in real-time to maximize their utility values. Existing VAs are unable to handle such dynamic goal-oriented situations. Methodology Due to the absence of any related dialogue dataset where such choice deviations are present, we have created a conversational dataset called Deviation adapted Virtual Agent(DevVA), with the manual annotation of its corresponding intents, slots, and sentiment labels. A Dynamic Goal Driven Dialogue Agent (DGDVA) has been developed by incorporating a Dynamic Goal Driven Module (GDM) on top of a deep reinforcement learning based dialogue manager. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88142874"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88142874/SETLabs_Briefings_Service_Oriented_Infrastructure"><img alt="Research paper thumbnail of SETLabs Briefings - Service Oriented Infrastructure" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88142874/SETLabs_Briefings_Service_Oriented_Infrastructure">SETLabs Briefings - Service Oriented Infrastructure</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">There is a palpable sense of anticipation about SOA the “new face” of enterprise computing and IT...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">There is a palpable sense of anticipation about SOA the “new face” of enterprise computing and IT services paradigm. Lot of debate is going on the important SOA issues like application models, service granularity, interfaces, re-use economics etc. The story that is left untold is how some of the so called esoteric technologies like Grid and virtualization make true SOA realizable in practice; well, almost. There are many significant technology shifts happening in those dark non-descript gargantuan warehouses that pass by the name “data centers” hosting thousands of computing resources. Research efforts worth millions of dollars are being spent on infrastructure layer virtualization and associated technologies. If we add to this the decades of extensive research done in the area of distributed heterogeneous computing or Grid, we now have a set of technologies that can usher in service orientation in the infrastructure fabric layer. We term this technology paradigm as Service Oriented...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88142874"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88142874"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88142874; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=88142874]").text(description); $(".js-view-count[data-work-id=88142874]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 88142874; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='88142874']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 88142874, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=88142874]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":88142874,"title":"SETLabs Briefings - Service Oriented Infrastructure","translated_title":"","metadata":{"abstract":"There is a palpable sense of anticipation about SOA the “new face” of enterprise computing and IT services paradigm. 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We term this technology paradigm as Service Oriented...","publication_date":{"day":null,"month":null,"year":2006,"errors":{}}},"translated_abstract":"There is a palpable sense of anticipation about SOA the “new face” of enterprise computing and IT services paradigm. Lot of debate is going on the important SOA issues like application models, service granularity, interfaces, re-use economics etc. The story that is left untold is how some of the so called esoteric technologies like Grid and virtualization make true SOA realizable in practice; well, almost. There are many significant technology shifts happening in those dark non-descript gargantuan warehouses that pass by the name “data centers” hosting thousands of computing resources. Research efforts worth millions of dollars are being spent on infrastructure layer virtualization and associated technologies. If we add to this the decades of extensive research done in the area of distributed heterogeneous computing or Grid, we now have a set of technologies that can usher in service orientation in the infrastructure fabric layer. We term this technology paradigm as Service Oriented...","internal_url":"https://www.academia.edu/88142874/SETLabs_Briefings_Service_Oriented_Infrastructure","translated_internal_url":"","created_at":"2022-10-08T20:59:44.938-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":4646101,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"SETLabs_Briefings_Service_Oriented_Infrastructure","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":4646101,"first_name":"Shubhashis","middle_initials":null,"last_name":"Sengupta","page_name":"ShubhashisSengupta","domain_name":"iimcal","created_at":"2013-06-24T15:07:02.645-07:00","display_name":"Shubhashis Sengupta","url":"https://iimcal.academia.edu/ShubhashisSengupta"},"attachments":[],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83927600"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83927600/An_Inference_Approach_To_Question_Answering_Over_Knowledge_Graphs"><img alt="Research paper thumbnail of An Inference Approach To Question Answering Over Knowledge Graphs" class="work-thumbnail" src="https://attachments.academia-assets.com/89117537/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83927600/An_Inference_Approach_To_Question_Answering_Over_Knowledge_Graphs">An Inference Approach To Question Answering Over Knowledge Graphs</a></div><div class="wp-workCard_item"><span>ArXiv</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Knowledge Graphs (KG) act as a great tool for holding distilled information from large natural la...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Knowledge Graphs (KG) act as a great tool for holding distilled information from large natural language text corpora. The problem of natural language querying over knowledge graphs is essential for the human consumption of this information. This problem is typically addressed by converting the natural language query to a structured query and then firing the structured query on the KG. Direct answering models over knowledge graphs in literature are very few. The query conversion models and direct models both require specific training data pertaining to the domain of the knowledge graph. In this work, we convert the problem of natural language querying over knowledge graphs to an inference problem over premise-hypothesis pairs. Using trained deep learning models for the converted proxy inferencing problem, we provide the solution for the original natural language querying problem. Our method achieves over 90% accuracy on MetaQA dataset, beating the existing state of the art. We also p...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b09eec719bf453fd4c9cca096045f120" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89117537,"asset_id":83927600,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89117537/download_file?st=MTczMjQ1NzQ4Niw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83927600"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83927600"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83927600; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83927600]").text(description); $(".js-view-count[data-work-id=83927600]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83927600; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83927600']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 83927600, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "b09eec719bf453fd4c9cca096045f120" } } $('.js-work-strip[data-work-id=83927600]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83927600,"title":"An Inference Approach To Question Answering Over Knowledge Graphs","translated_title":"","metadata":{"abstract":"Knowledge Graphs (KG) act as a great tool for holding distilled information from large natural language text corpora. The problem of natural language querying over knowledge graphs is essential for the human consumption of this information. This problem is typically addressed by converting the natural language query to a structured query and then firing the structured query on the KG. Direct answering models over knowledge graphs in literature are very few. The query conversion models and direct models both require specific training data pertaining to the domain of the knowledge graph. In this work, we convert the problem of natural language querying over knowledge graphs to an inference problem over premise-hypothesis pairs. Using trained deep learning models for the converted proxy inferencing problem, we provide the solution for the original natural language querying problem. Our method achieves over 90% accuracy on MetaQA dataset, beating the existing state of the art. 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Using trained deep learning models for the converted proxy inferencing problem, we provide the solution for the original natural language querying problem. Our method achieves over 90% accuracy on MetaQA dataset, beating the existing state of the art. 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We describe a use case around Luggage handling for a commercial airline where we answer users queries regarding various policies such as luggage dimensions, restrictions on carry-on items, travel routes etc. We have encoded the domain entities, relationships, processes and polices in the knowledge graph and created a generic semantic natural language processing engine to process user queries and retrieve the correct results from a knowledge graph.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fb0a850ee8f2d54a739ceb5155aaf6c1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89117526,"asset_id":83927596,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89117526/download_file?st=MTczMjQ1NzQ4Niw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83927596"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83927596"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83927596; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83927596]").text(description); $(".js-view-count[data-work-id=83927596]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83927596; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83927596']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 83927596, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "fb0a850ee8f2d54a739ceb5155aaf6c1" } } $('.js-work-strip[data-work-id=83927596]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83927596,"title":"Unscripted Conversation through Knowledge Graph","translated_title":"","metadata":{"abstract":"In this paper, we introduce “unscripted conversation” free form dialog over a domain knowledge graph. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="586425" id="papers"><div class="js-work-strip profile--work_container" data-work-id="106373319"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/106373319/Predicting_Reputation_Score_of_Users_in_Stack_overflow_with_Alternate_Data"><img alt="Research paper thumbnail of Predicting Reputation Score of Users in Stack-overflow with Alternate Data" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/106373319/Predicting_Reputation_Score_of_Users_in_Stack_overflow_with_Alternate_Data">Predicting Reputation Score of Users in Stack-overflow with Alternate Data</a></div><div class="wp-workCard_item"><span>Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="106373319"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="106373319"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 106373319; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="101436549"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/101436549/Neural_response_generation_for_task_completion_using_conversational_knowledge_graph"><img alt="Research paper thumbnail of Neural response generation for task completion using conversational knowledge graph" class="work-thumbnail" src="https://attachments.academia-assets.com/101978991/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/101436549/Neural_response_generation_for_task_completion_using_conversational_knowledge_graph">Neural response generation for task completion using conversational knowledge graph</a></div><div class="wp-workCard_item"><span>PLOS ONE</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Effective dialogue generation for task completion is challenging to build. The task requires the ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Effective dialogue generation for task completion is challenging to build. The task requires the response generation system to generate the responses consistent with intent and slot values, have diversity in response and be able to handle multiple domains. The response also needs to be context relevant with respect to the previous utterances in the conversation. In this paper, we build six different models containing Bi-directional Long Short Term Memory (Bi-LSTM) and Bidirectional Encoder Representations from Transformers (BERT) based encoders. To effectively generate the correct slot values, we implement a copy mechanism at the decoder side. To capture the conversation context and the current state of the conversation we introduce a simple heuristic to build a conversational knowledge graph. Using this novel algorithm we are able to capture important aspects in a conversation. This conversational knowledge-graph is then used by our response generation model to generate more releva...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5757d3f965601aaf99f484f2b5e6fb03" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":101978991,"asset_id":101436549,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/101978991/download_file?st=MTczMjQ1NzQ4Niw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="101436549"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="101436549"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 101436549; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=101436549]").text(description); $(".js-view-count[data-work-id=101436549]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 101436549; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='101436549']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 101436549, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "5757d3f965601aaf99f484f2b5e6fb03" } } $('.js-work-strip[data-work-id=101436549]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":101436549,"title":"Neural response generation for task completion using conversational knowledge graph","translated_title":"","metadata":{"abstract":"Effective dialogue generation for task completion is challenging to build. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="101436548"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/101436548/Towards_Sentiment_and_Emotion_aided_Intent_Detection"><img alt="Research paper thumbnail of Towards Sentiment and Emotion aided Intent Detection" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/101436548/Towards_Sentiment_and_Emotion_aided_Intent_Detection">Towards Sentiment and Emotion aided Intent Detection</a></div><div class="wp-workCard_item"><span>2022 26th International Conference on Pattern Recognition (ICPR)</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="101436548"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="101436548"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 101436548; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="101436544"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/101436544/Reinforcing_personalized_persuasion_in_task_oriented_virtual_sales_assistant"><img alt="Research paper thumbnail of Reinforcing personalized persuasion in task-oriented virtual sales assistant" class="work-thumbnail" src="https://attachments.academia-assets.com/101978982/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/101436544/Reinforcing_personalized_persuasion_in_task_oriented_virtual_sales_assistant">Reinforcing personalized persuasion in task-oriented virtual sales assistant</a></div><div class="wp-workCard_item"><span>PLOS ONE</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Purpose Existing task-oriented virtual agents can assist users with simple tasks like ticket book...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Purpose Existing task-oriented virtual agents can assist users with simple tasks like ticket booking, hotel reservations, etc. effectively and with high confidence. These virtual assistants, however, assume specific, predictable end-user behavior, such as predefined/servable objectives, which results in conversation failures in challenging situations, such as when goals are unavailable. Methodology Inspired by the practice and its efficacy, we propose an end-to-end framework for task-oriented persuasive dialogue generation that combines pre-training and reinforcement learning for generating context-aware persuasive responses. We utilize four novel rewards to improve consistency and repetitiveness in generated responses. Additionally, a meta-learning strategy has also been utilized to make the model parameters better for domain adaptation. Furthermore, we also curate a personalized persuasive dialogue (PPD) corpus, which contains utterance-level intent, slot, sentiment, and persuasio...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="915583f80b51299045779bc50ef15967" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":101978982,"asset_id":101436544,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/101978982/download_file?st=MTczMjQ1NzQ4Niw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="101436544"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="101436544"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 101436544; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=101436544]").text(description); $(".js-view-count[data-work-id=101436544]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 101436544; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='101436544']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 101436544, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "915583f80b51299045779bc50ef15967" } } $('.js-work-strip[data-work-id=101436544]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":101436544,"title":"Reinforcing personalized persuasion in task-oriented virtual sales assistant","translated_title":"","metadata":{"abstract":"Purpose Existing task-oriented virtual agents can assist users with simple tasks like ticket booking, hotel reservations, etc. effectively and with high confidence. These virtual assistants, however, assume specific, predictable end-user behavior, such as predefined/servable objectives, which results in conversation failures in challenging situations, such as when goals are unavailable. Methodology Inspired by the practice and its efficacy, we propose an end-to-end framework for task-oriented persuasive dialogue generation that combines pre-training and reinforcement learning for generating context-aware persuasive responses. We utilize four novel rewards to improve consistency and repetitiveness in generated responses. Additionally, a meta-learning strategy has also been utilized to make the model parameters better for domain adaptation. Furthermore, we also curate a personalized persuasive dialogue (PPD) corpus, which contains utterance-level intent, slot, sentiment, and persuasio...","publisher":"Public Library of Science (PLoS)","publication_name":"PLOS ONE"},"translated_abstract":"Purpose Existing task-oriented virtual agents can assist users with simple tasks like ticket booking, hotel reservations, etc. effectively and with high confidence. These virtual assistants, however, assume specific, predictable end-user behavior, such as predefined/servable objectives, which results in conversation failures in challenging situations, such as when goals are unavailable. Methodology Inspired by the practice and its efficacy, we propose an end-to-end framework for task-oriented persuasive dialogue generation that combines pre-training and reinforcement learning for generating context-aware persuasive responses. We utilize four novel rewards to improve consistency and repetitiveness in generated responses. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88142881"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88142881/From_Opinion_Mining_to_Improvement_Mining_Understanding_Product_Improvements_from_User_Reviews"><img alt="Research paper thumbnail of From Opinion Mining to Improvement Mining : Understanding Product Improvements from User Reviews" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88142881/From_Opinion_Mining_to_Improvement_Mining_Understanding_Product_Improvements_from_User_Reviews">From Opinion Mining to Improvement Mining : Understanding Product Improvements from User Reviews</a></div><div class="wp-workCard_item"><span>Forum for Information Retrieval Evaluation</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A valuable trove of information exists for product(s) or services online via user opinions like d...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A valuable trove of information exists for product(s) or services online via user opinions like detailed reviews provided by customers on popular e-commerce websites. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88142877"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88142877/Combining_exogenous_and_endogenous_signals_with_a_semi_supervised_co_attention_network_for_early_detection_of_COVID_19_fake_tweets"><img alt="Research paper thumbnail of Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets" class="work-thumbnail" src="https://attachments.academia-assets.com/92175971/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88142877/Combining_exogenous_and_endogenous_signals_with_a_semi_supervised_co_attention_network_for_early_detection_of_COVID_19_fake_tweets">Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets</a></div><div class="wp-workCard_item"><span>ArXiv</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Fake tweets are observed to be ever-increasing, demanding immediate countermeasures to combat the...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Fake tweets are observed to be ever-increasing, demanding immediate countermeasures to combat their spread. During COVID-19, tweets with misinformation should be flagged and neutralised in their early stages to mitigate the damages. Most of the existing methods for early detection of fake news assume to have enough propagation information for large labelled tweets – which may not be an ideal setting for cases like COVID-19 where both aspects are largely absent. In this work, we present ENDEMIC, a novel early detection model which leverages exogenous and endogenous signals related to tweets, while learning on limited labelled data. We first develop a novel dataset, called ECTF for early COVID-19 Twitter fake news, with additional behavioural test-sets to validate early detection. We build a heterogeneous graph with follower-followee, user-tweet, and tweet-retweet connections and train a graph embedding model to aggregate propagation information. Graph embeddings and contextual featur...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ac564ae0d8e342c1c34efe01bbf1a972" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92175971,"asset_id":88142877,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92175971/download_file?st=MTczMjQ1NzQ4Niw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88142877"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88142877"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88142877; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=88142877]").text(description); $(".js-view-count[data-work-id=88142877]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 88142877; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='88142877']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 88142877, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ac564ae0d8e342c1c34efe01bbf1a972" } } $('.js-work-strip[data-work-id=88142877]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":88142877,"title":"Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets","translated_title":"","metadata":{"abstract":"Fake tweets are observed to be ever-increasing, demanding immediate countermeasures to combat their spread. 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Graph embeddings and contextual featur...","publisher":"PAKDD","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"ArXiv"},"translated_abstract":"Fake tweets are observed to be ever-increasing, demanding immediate countermeasures to combat their spread. During COVID-19, tweets with misinformation should be flagged and neutralised in their early stages to mitigate the damages. Most of the existing methods for early detection of fake news assume to have enough propagation information for large labelled tweets – which may not be an ideal setting for cases like COVID-19 where both aspects are largely absent. In this work, we present ENDEMIC, a novel early detection model which leverages exogenous and endogenous signals related to tweets, while learning on limited labelled data. We first develop a novel dataset, called ECTF for early COVID-19 Twitter fake news, with additional behavioural test-sets to validate early detection. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88142876"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88142876/A_dynamic_goal_adapted_task_oriented_dialogue_agent"><img alt="Research paper thumbnail of A dynamic goal adapted task oriented dialogue agent" class="work-thumbnail" src="https://attachments.academia-assets.com/92175969/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88142876/A_dynamic_goal_adapted_task_oriented_dialogue_agent">A dynamic goal adapted task oriented dialogue agent</a></div><div class="wp-workCard_item"><span>PLOS ONE</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Purpose Existing virtual agents (VAs) present in dialogue systems are either information retrieva...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Purpose Existing virtual agents (VAs) present in dialogue systems are either information retrieval based or static goal-driven. However, in real-world situations, end-users might not have a known and fixed goal beforehand for the task, i.e., they may upgrade/downgrade/update their goal components in real-time to maximize their utility values. Existing VAs are unable to handle such dynamic goal-oriented situations. Methodology Due to the absence of any related dialogue dataset where such choice deviations are present, we have created a conversational dataset called Deviation adapted Virtual Agent(DevVA), with the manual annotation of its corresponding intents, slots, and sentiment labels. A Dynamic Goal Driven Dialogue Agent (DGDVA) has been developed by incorporating a Dynamic Goal Driven Module (GDM) on top of a deep reinforcement learning based dialogue manager. In the course of a conversation, the user sentiment provides grounded feedback about agent behavior, including goal serv...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="daf8e0fa595f635f275083e340aba86b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92175969,"asset_id":88142876,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92175969/download_file?st=MTczMjQ1NzQ4Niw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88142876"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88142876"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88142876; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=88142876]").text(description); $(".js-view-count[data-work-id=88142876]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 88142876; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='88142876']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 88142876, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "daf8e0fa595f635f275083e340aba86b" } } $('.js-work-strip[data-work-id=88142876]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":88142876,"title":"A dynamic goal adapted task oriented dialogue agent","translated_title":"","metadata":{"abstract":"Purpose Existing virtual agents (VAs) present in dialogue systems are either information retrieval based or static goal-driven. However, in real-world situations, end-users might not have a known and fixed goal beforehand for the task, i.e., they may upgrade/downgrade/update their goal components in real-time to maximize their utility values. Existing VAs are unable to handle such dynamic goal-oriented situations. Methodology Due to the absence of any related dialogue dataset where such choice deviations are present, we have created a conversational dataset called Deviation adapted Virtual Agent(DevVA), with the manual annotation of its corresponding intents, slots, and sentiment labels. A Dynamic Goal Driven Dialogue Agent (DGDVA) has been developed by incorporating a Dynamic Goal Driven Module (GDM) on top of a deep reinforcement learning based dialogue manager. In the course of a conversation, the user sentiment provides grounded feedback about agent behavior, including goal serv...","publisher":"Public Library of Science (PLoS)","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"PLOS ONE"},"translated_abstract":"Purpose Existing virtual agents (VAs) present in dialogue systems are either information retrieval based or static goal-driven. However, in real-world situations, end-users might not have a known and fixed goal beforehand for the task, i.e., they may upgrade/downgrade/update their goal components in real-time to maximize their utility values. Existing VAs are unable to handle such dynamic goal-oriented situations. Methodology Due to the absence of any related dialogue dataset where such choice deviations are present, we have created a conversational dataset called Deviation adapted Virtual Agent(DevVA), with the manual annotation of its corresponding intents, slots, and sentiment labels. A Dynamic Goal Driven Dialogue Agent (DGDVA) has been developed by incorporating a Dynamic Goal Driven Module (GDM) on top of a deep reinforcement learning based dialogue manager. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88142874"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88142874/SETLabs_Briefings_Service_Oriented_Infrastructure"><img alt="Research paper thumbnail of SETLabs Briefings - Service Oriented Infrastructure" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88142874/SETLabs_Briefings_Service_Oriented_Infrastructure">SETLabs Briefings - Service Oriented Infrastructure</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">There is a palpable sense of anticipation about SOA the “new face” of enterprise computing and IT...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">There is a palpable sense of anticipation about SOA the “new face” of enterprise computing and IT services paradigm. Lot of debate is going on the important SOA issues like application models, service granularity, interfaces, re-use economics etc. The story that is left untold is how some of the so called esoteric technologies like Grid and virtualization make true SOA realizable in practice; well, almost. There are many significant technology shifts happening in those dark non-descript gargantuan warehouses that pass by the name “data centers” hosting thousands of computing resources. Research efforts worth millions of dollars are being spent on infrastructure layer virtualization and associated technologies. If we add to this the decades of extensive research done in the area of distributed heterogeneous computing or Grid, we now have a set of technologies that can usher in service orientation in the infrastructure fabric layer. We term this technology paradigm as Service Oriented...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88142874"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88142874"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88142874; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=88142874]").text(description); $(".js-view-count[data-work-id=88142874]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 88142874; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='88142874']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 88142874, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=88142874]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":88142874,"title":"SETLabs Briefings - Service Oriented Infrastructure","translated_title":"","metadata":{"abstract":"There is a palpable sense of anticipation about SOA the “new face” of enterprise computing and IT services paradigm. 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We term this technology paradigm as Service Oriented...","publication_date":{"day":null,"month":null,"year":2006,"errors":{}}},"translated_abstract":"There is a palpable sense of anticipation about SOA the “new face” of enterprise computing and IT services paradigm. Lot of debate is going on the important SOA issues like application models, service granularity, interfaces, re-use economics etc. The story that is left untold is how some of the so called esoteric technologies like Grid and virtualization make true SOA realizable in practice; well, almost. There are many significant technology shifts happening in those dark non-descript gargantuan warehouses that pass by the name “data centers” hosting thousands of computing resources. Research efforts worth millions of dollars are being spent on infrastructure layer virtualization and associated technologies. If we add to this the decades of extensive research done in the area of distributed heterogeneous computing or Grid, we now have a set of technologies that can usher in service orientation in the infrastructure fabric layer. We term this technology paradigm as Service Oriented...","internal_url":"https://www.academia.edu/88142874/SETLabs_Briefings_Service_Oriented_Infrastructure","translated_internal_url":"","created_at":"2022-10-08T20:59:44.938-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":4646101,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"SETLabs_Briefings_Service_Oriented_Infrastructure","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":4646101,"first_name":"Shubhashis","middle_initials":null,"last_name":"Sengupta","page_name":"ShubhashisSengupta","domain_name":"iimcal","created_at":"2013-06-24T15:07:02.645-07:00","display_name":"Shubhashis Sengupta","url":"https://iimcal.academia.edu/ShubhashisSengupta"},"attachments":[],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83927600"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83927600/An_Inference_Approach_To_Question_Answering_Over_Knowledge_Graphs"><img alt="Research paper thumbnail of An Inference Approach To Question Answering Over Knowledge Graphs" class="work-thumbnail" src="https://attachments.academia-assets.com/89117537/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83927600/An_Inference_Approach_To_Question_Answering_Over_Knowledge_Graphs">An Inference Approach To Question Answering Over Knowledge Graphs</a></div><div class="wp-workCard_item"><span>ArXiv</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Knowledge Graphs (KG) act as a great tool for holding distilled information from large natural la...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Knowledge Graphs (KG) act as a great tool for holding distilled information from large natural language text corpora. The problem of natural language querying over knowledge graphs is essential for the human consumption of this information. This problem is typically addressed by converting the natural language query to a structured query and then firing the structured query on the KG. Direct answering models over knowledge graphs in literature are very few. The query conversion models and direct models both require specific training data pertaining to the domain of the knowledge graph. In this work, we convert the problem of natural language querying over knowledge graphs to an inference problem over premise-hypothesis pairs. Using trained deep learning models for the converted proxy inferencing problem, we provide the solution for the original natural language querying problem. Our method achieves over 90% accuracy on MetaQA dataset, beating the existing state of the art. We also p...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b09eec719bf453fd4c9cca096045f120" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89117537,"asset_id":83927600,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89117537/download_file?st=MTczMjQ1NzQ4Niw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83927600"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83927600"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83927600; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83927600]").text(description); $(".js-view-count[data-work-id=83927600]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83927600; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83927600']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 83927600, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "b09eec719bf453fd4c9cca096045f120" } } $('.js-work-strip[data-work-id=83927600]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83927600,"title":"An Inference Approach To Question Answering Over Knowledge Graphs","translated_title":"","metadata":{"abstract":"Knowledge Graphs (KG) act as a great tool for holding distilled information from large natural language text corpora. The problem of natural language querying over knowledge graphs is essential for the human consumption of this information. This problem is typically addressed by converting the natural language query to a structured query and then firing the structured query on the KG. Direct answering models over knowledge graphs in literature are very few. The query conversion models and direct models both require specific training data pertaining to the domain of the knowledge graph. In this work, we convert the problem of natural language querying over knowledge graphs to an inference problem over premise-hypothesis pairs. Using trained deep learning models for the converted proxy inferencing problem, we provide the solution for the original natural language querying problem. Our method achieves over 90% accuracy on MetaQA dataset, beating the existing state of the art. 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Using trained deep learning models for the converted proxy inferencing problem, we provide the solution for the original natural language querying problem. Our method achieves over 90% accuracy on MetaQA dataset, beating the existing state of the art. 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