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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="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="50446196" href="https://www.academia.edu/Documents/in/Speech_Processing"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://independent.academia.edu/JoelTetreault","location":"/JoelTetreault","scheme":"https","host":"independent.academia.edu","port":null,"pathname":"/JoelTetreault","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":["Speech Processing"]}" data-trace="false" data-dom-id="Pill-react-component-73c84cb7-1bde-4688-b512-7391a4bc0cbc"></div> <div id="Pill-react-component-73c84cb7-1bde-4688-b512-7391a4bc0cbc"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="50446196" href="https://www.academia.edu/Documents/in/Speech_Recognition"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Speech Recognition"]}" data-trace="false" data-dom-id="Pill-react-component-095efaaf-e10e-4aed-aaa1-90fda6d14db3"></div> <div id="Pill-react-component-095efaaf-e10e-4aed-aaa1-90fda6d14db3"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="50446196" href="https://www.academia.edu/Documents/in/Speech-Language_Pathology_Communication_Disorders"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Speech-Language 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data-props="{"color":"gray","children":["Computer Science"]}" data-trace="false" data-dom-id="Pill-react-component-2d842afb-2076-41d9-9dd1-2d94f22f0102"></div> <div id="Pill-react-component-2d842afb-2076-41d9-9dd1-2d94f22f0102"></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 Joel Tetreault</h3></div><div class="js-work-strip profile--work_container" data-work-id="26513540"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/26513540/Re_examining_machine_translation_metrics_for_paraphrase_identification"><img alt="Research paper thumbnail of Re-examining machine translation metrics for paraphrase identification" 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" rel="nofollow" href="https://www.academia.edu/26513540/Re_examining_machine_translation_metrics_for_paraphrase_identification">Re-examining machine translation metrics for paraphrase identification</a></div><div class="wp-workCard_item"><span>Proceedings of the 2012 Conference of the North American Chapter of the Association For Computational Linguistics Human Language Technologies</span><span>, Jun 3, 2012</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract We propose to re-examine the hypothesis that automated metrics developed for MT evaluati...</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">Abstract We propose to re-examine the hypothesis that automated metrics developed for MT evaluation can prove useful for paraphrase identification in light of the significant work on the development of new MT metrics over the last 4 years. We show that a meta-classifier trained using nothing but recent MT metrics outperforms all previous paraphrase identification approaches on the Microsoft Research Paraphrase corpus. In addition, we apply our system to a second corpus developed for the task of plagiarism detection and ...</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="26513540"><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="26513540"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513540; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=26513540]").text(description); $(".js-view-count[data-work-id=26513540]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 26513540; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='26513540']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 26513540, 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" } } $('.js-work-strip[data-work-id=26513540]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":26513540,"title":"Re-examining machine translation metrics for paraphrase identification","translated_title":"","metadata":{"abstract":"Abstract We propose to re-examine the hypothesis that automated metrics developed for MT evaluation can prove useful for paraphrase identification in light of the significant work on the development of new MT metrics over the last 4 years. 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In addition, we apply our system to a second corpus developed for the task of plagiarism detection and ...","internal_url":"https://www.academia.edu/26513540/Re_examining_machine_translation_metrics_for_paraphrase_identification","translated_internal_url":"","created_at":"2016-06-26T11:24:37.496-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":50446196,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Re_examining_machine_translation_metrics_for_paraphrase_identification","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":50446196,"first_name":"Joel","middle_initials":null,"last_name":"Tetreault","page_name":"JoelTetreault","domain_name":"independent","created_at":"2016-06-25T07:02:03.031-07:00","display_name":"Joel Tetreault","url":"https://independent.academia.edu/JoelTetreault"},"attachments":[],"research_interests":[{"id":4696,"name":"Machine Translation","url":"https://www.academia.edu/Documents/in/Machine_Translation"}],"urls":[{"id":7253734,"url":"http://dl.acm.org/citation.cfm?id=2382055"}]}, 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="26513539"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/26513539/Rethinking_grammatical_error_annotation_and_evaluation_with_the_Amazon_Mechanical_Turk"><img alt="Research paper thumbnail of Rethinking grammatical error annotation and evaluation with the Amazon Mechanical Turk" 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" rel="nofollow" href="https://www.academia.edu/26513539/Rethinking_grammatical_error_annotation_and_evaluation_with_the_Amazon_Mechanical_Turk">Rethinking grammatical error annotation and evaluation with the Amazon Mechanical Turk</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract In this paper we present results from two pilot studies which show that using the Amazon...</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">Abstract In this paper we present results from two pilot studies which show that using the Amazon Mechanical Turk for preposition error annotation is as effective as using trained raters, but at a fraction of the time and cost. Based on these results, we propose a new evaluation method which makes it feasible to compare two error detection systems tested on different learner data sets.</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="26513539"><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="26513539"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513539; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=26513539]").text(description); $(".js-view-count[data-work-id=26513539]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 26513539; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='26513539']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 26513539, 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" } } $('.js-work-strip[data-work-id=26513539]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":26513539,"title":"Rethinking grammatical error annotation and evaluation with the Amazon Mechanical Turk","translated_title":"","metadata":{"abstract":"Abstract In this paper we present results from two pilot studies which show that using the Amazon Mechanical Turk for preposition error annotation is as effective as using trained raters, but at a fraction of the time and cost. 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Based on these results, we propose a new evaluation method which makes it feasible to compare two error detection systems tested on different learner data sets.","internal_url":"https://www.academia.edu/26513539/Rethinking_grammatical_error_annotation_and_evaluation_with_the_Amazon_Mechanical_Turk","translated_internal_url":"","created_at":"2016-06-26T11:24:37.289-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":50446196,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Rethinking_grammatical_error_annotation_and_evaluation_with_the_Amazon_Mechanical_Turk","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":50446196,"first_name":"Joel","middle_initials":null,"last_name":"Tetreault","page_name":"JoelTetreault","domain_name":"independent","created_at":"2016-06-25T07:02:03.031-07:00","display_name":"Joel Tetreault","url":"https://independent.academia.edu/JoelTetreault"},"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="26513538"><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/26513538/Towards_using_structural_events_to_assess_non_native_speech"><img alt="Research paper thumbnail of Towards using structural events to assess non-native speech" class="work-thumbnail" src="https://attachments.academia-assets.com/46809878/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/26513538/Towards_using_structural_events_to_assess_non_native_speech">Towards using structural events to assess non-native speech</a></div><div class="wp-workCard_item"><span>Proceedings of the Naacl Hlt 2010 Fifth Workshop on Innovative Use of Nlp For Building Educational Applications</span><span>, Jun 5, 2010</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c4cbee7c5d2a9b0929bcd9d8948fd0ad" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":46809878,"asset_id":26513538,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/46809878/download_file?st=MTczMjQ2ODEyMSw4LjIyMi4yMDguMTQ2&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="26513538"><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="26513538"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513538; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=26513538]").text(description); $(".js-view-count[data-work-id=26513538]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 26513538; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='26513538']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 26513538, 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); 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Using a set of transcribed audio files collected from the TOEFL Practice Test Online (TPO), we conducted a sophisticated annotation of structural events, including clause boundaries and types, as well as disfluencies. Based on words and the annotated structural events, we extracted features related to syntactic complexity, e.g., the mean length of clause (MLC) and dependent clause frequency (DEPC), and a feature related to disfluencies, the interruption point frequency per clause (IPC). Among these features, the IPC shows the highest correlation with holistic scores (r = −0.344). Furthermore, we increased the correlation with human scores by normalizing IPC by (1) MLC (r = −0.386), (2) DEPC (r = −0.429), and (3) both (r = −0.462). In this research, the features derived from structural events of speech transcriptions are found to predict holistic scores measuring speaking proficiency. 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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="26513532"><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/26513532/Exploiting_syntactic_and_distributional_information_for_spelling_correction_with_web_scale_n_gram_models"><img alt="Research paper thumbnail of Exploiting syntactic and distributional information for spelling correction with web-scale n-gram models" class="work-thumbnail" src="https://attachments.academia-assets.com/46809875/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/26513532/Exploiting_syntactic_and_distributional_information_for_spelling_correction_with_web_scale_n_gram_models">Exploiting syntactic and distributional information for spelling correction with web-scale n-gram models</a></div><div class="wp-workCard_item"><span>Proceedings of the Conference on Empirical Methods in Natural Language Processing</span><span>, 2011</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cc2cb6ca11b1775818791e4ff9074e99" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":46809875,"asset_id":26513532,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/46809875/download_file?st=MTczMjQ2ODEyMSw4LjIyMi4yMDguMTQ2&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="26513532"><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="26513532"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513532; 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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="26513531"><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/26513531/Estimating_the_Reliability_of_MDP_Policies_a_Confidence_Interval_Approach"><img alt="Research paper thumbnail of Estimating the Reliability of MDP Policies: a Confidence Interval Approach" class="work-thumbnail" src="https://attachments.academia-assets.com/46809876/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/26513531/Estimating_the_Reliability_of_MDP_Policies_a_Confidence_Interval_Approach">Estimating the Reliability of MDP Policies: a Confidence Interval Approach</a></div><div class="wp-workCard_item"><span>Naacl</span><span>, 2007</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7b27d2027a4b304ca98e72627acd6b0e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":46809876,"asset_id":26513531,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/46809876/download_file?st=MTczMjQ2ODEyMSw4LjIyMi4yMDguMTQ2&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="26513531"><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="26513531"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513531; 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In this paper we present a methodology for numerically constructing confidence intervals for the expected cumulative reward for a learned policy. These intervals are used to (1) better assess the reliability of the expected cumulative reward, and (2) perform a refined comparison between policies derived from different Markov Decision Processes (MDP) models. We applied this methodology to a prior experiment where the goal was to select the best features to include in the MDP statespace. 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Resu...</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">We evaluate the effect of adding parse fea- tures to a leading model of preposition us- age. Results show a significant improve- ment in the preposition selection task on native speaker text and a modest increment in precision and recall in an ESL error de- tection task. Analysis of the parser output indicates that it is robust enough in the</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b943153eaac658dd14cfc81c1db847a2" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":46809820,"asset_id":26513528,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/46809820/download_file?st=MTczMjQ2ODEyMSw4LjIyMi4yMDguMTQ2&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="26513528"><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="26513528"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513528; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=26513528]").text(description); $(".js-view-count[data-work-id=26513528]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 26513528; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='26513528']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 26513528, 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: "b943153eaac658dd14cfc81c1db847a2" } } $('.js-work-strip[data-work-id=26513528]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":26513528,"title":"Using Parse Features for Preposition Selection and Error Detection","translated_title":"","metadata":{"abstract":"We evaluate the effect of adding parse fea- tures to a leading model of preposition us- age. 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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="26513527"><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/26513527/Using_an_Error_Annotated_Learner_Corpus_to_Develop_an_ESL_EFL_Error_Correction_System"><img alt="Research paper thumbnail of Using an Error-Annotated Learner Corpus to Develop an ESL/EFL Error Correction System" class="work-thumbnail" src="https://attachments.academia-assets.com/46809872/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/26513527/Using_an_Error_Annotated_Learner_Corpus_to_Develop_an_ESL_EFL_Error_Correction_System">Using an Error-Annotated Learner Corpus to Develop an ESL/EFL Error Correction System</a></div><div class="wp-workCard_item"><span>Language Resources and Evaluation</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents research on building a model of grammatical error correction, for preposition...</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">This paper presents research on building a model of grammatical error correction, for preposition errors in particular , in English text produced by language learners. Unlike most previous work which trains a statistical classifier exclusively on well-for med text written by native speakers, we train a classifier on a large-scale, error-tagged corpus of English essays written by EFL learners, relying</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="99c4b529c4fbcb51a3fa3b2bbef93c3d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":46809872,"asset_id":26513527,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/46809872/download_file?st=MTczMjQ2ODEyMSw4LjIyMi4yMDguMTQ2&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="26513527"><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="26513527"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513527; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=26513527]").text(description); $(".js-view-count[data-work-id=26513527]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 26513527; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='26513527']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 26513527, 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: "99c4b529c4fbcb51a3fa3b2bbef93c3d" } } $('.js-work-strip[data-work-id=26513527]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":26513527,"title":"Using an Error-Annotated Learner Corpus to Develop an ESL/EFL Error Correction System","translated_title":"","metadata":{"abstract":"This paper presents research on building a model of grammatical error correction, for preposition errors in particular , in English text produced by language learners. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="26513526"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/26513526/Dialogue_Structure_and_Pronoun_Resolution"><img alt="Research paper thumbnail of Dialogue Structure and Pronoun Resolution" class="work-thumbnail" src="https://attachments.academia-assets.com/46809818/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/26513526/Dialogue_Structure_and_Pronoun_Resolution">Dialogue Structure and Pronoun Resolution</a></div><div class="wp-workCard_item"><span>Discourse Anaphora and Anaphor Resolution Colloquium</span><span>, 2004</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents an empirical evaluation of a pronoun resolution algorithm augmented with disc...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper presents an empirical evaluation of a pronoun resolution algorithm augmented with discourse segmentation information. Past work has shown that segmenting discourse can aid in pronoun resolution by making potentially erroneous candidates inaccessible to a pronoun&#39;s search. 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$(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="26513525"><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/26513525/An_Empirical_Evaluation_of_Pronoun_Resolution_and_Clausal_Structure"><img alt="Research paper thumbnail of An Empirical Evaluation of Pronoun Resolution and Clausal Structure" class="work-thumbnail" src="https://attachments.academia-assets.com/46809816/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/26513525/An_Empirical_Evaluation_of_Pronoun_Resolution_and_Clausal_Structure">An Empirical Evaluation of Pronoun Resolution and Clausal Structure</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents an automated empiri- cal evaluation of the relationship between clausal struc...</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">This paper presents an automated empiri- cal evaluation of the relationship between clausal structure and pronominal refer- ence. Past work has theorized that in- corporating discourse structure can con- strain the search space in the resolution of pronouns since discourse segments, and thus potential antecedents, can be made inaccessible as the discourse pro- gresses and the focus changes. How- ever,</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8340f9bbec4491ca0620fcd041be748a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":46809816,"asset_id":26513525,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/46809816/download_file?st=MTczMjQ2ODEyMSw4LjIyMi4yMDguMTQ2&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="26513525"><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="26513525"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513525; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=26513525]").text(description); $(".js-view-count[data-work-id=26513525]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 26513525; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='26513525']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 26513525, 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: "8340f9bbec4491ca0620fcd041be748a" } } $('.js-work-strip[data-work-id=26513525]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":26513525,"title":"An Empirical Evaluation of Pronoun Resolution and Clausal Structure","translated_title":"","metadata":{"abstract":"This paper presents an automated empiri- cal evaluation of the relationship between clausal structure and pronominal refer- ence. 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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="26513524"><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/26513524/Incremental_Parsing_with_Reference_Interaction"><img alt="Research paper thumbnail of Incremental Parsing with Reference Interaction" class="work-thumbnail" src="https://attachments.academia-assets.com/46809870/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/26513524/Incremental_Parsing_with_Reference_Interaction">Incremental Parsing with Reference Interaction</a></div><div class="wp-workCard_item"><span>Meeting of the Association for Computational Linguistics</span><span>, 2000</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We present a general architecture for incremen- tal interaction between modules in a speech-to- i...</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">We present a general architecture for incremen- tal interaction between modules in a speech-to- intention continuous understanding dialogue sys- tem. This architecture is then instantiated in the form of an incremental parser which receives suit- ability feedback on NP constituents from a refer- ence resolution module. Oracle results indicate that perfect NP suitability judgments can provide a labelled-bracket error reduction</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="66e074e002357ba21b1893416c758c5f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":46809870,"asset_id":26513524,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/46809870/download_file?st=MTczMjQ2ODEyMSw4LjIyMi4yMDguMTQ2&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="26513524"><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="26513524"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513524; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=26513524]").text(description); $(".js-view-count[data-work-id=26513524]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 26513524; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='26513524']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 26513524, 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: "66e074e002357ba21b1893416c758c5f" } } $('.js-work-strip[data-work-id=26513524]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":26513524,"title":"Incremental Parsing with Reference Interaction","translated_title":"","metadata":{"abstract":"We present a general architecture for incremen- tal interaction between modules in a speech-to- intention continuous understanding dialogue sys- tem. 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While policy development is very important, choosing the best fea- tures to model the user state is equally im- portant since it impacts the</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c21b766579194477b99e9a22a5e5f153" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":46809873,"asset_id":26513523,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/46809873/download_file?st=MTczMjQ2ODEyMSw4LjIyMi4yMDguMTQ2&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="26513523"><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="26513523"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513523; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=26513523]").text(description); $(".js-view-count[data-work-id=26513523]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 26513523; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='26513523']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 26513523, 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: "c21b766579194477b99e9a22a5e5f153" } } $('.js-work-strip[data-work-id=26513523]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":26513523,"title":"Comparing the Utility of State Features in Spoken Dialogue Using Reinforcement Learning","translated_title":"","metadata":{"abstract":"Recent work in designing spoken dialogue systems has focused on using Reinforce- ment Learning to automatically learn the best action for a system to take at any point in the dialogue to maximize dia- logue success. 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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="26513522"><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/26513522/Using_Reinforcement_Learning_to_Build_a_Better_Model_of_Dialogue_State"><img alt="Research paper thumbnail of Using Reinforcement Learning to Build a Better Model of Dialogue State" class="work-thumbnail" src="https://attachments.academia-assets.com/46809869/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/26513522/Using_Reinforcement_Learning_to_Build_a_Better_Model_of_Dialogue_State">Using Reinforcement Learning to Build a Better Model of Dialogue State</a></div><div class="wp-workCard_item"><span>Eacl</span><span>, 2006</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="67bdf3e0baed6f4d0aa1f4506860fd98" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":46809869,"asset_id":26513522,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/46809869/download_file?st=MTczMjQ2ODEyMSw4LjIyMi4yMDguMTQ2&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="26513522"><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="26513522"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513522; 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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="5428461" id="papers"><div class="js-work-strip profile--work_container" data-work-id="26513540"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/26513540/Re_examining_machine_translation_metrics_for_paraphrase_identification"><img alt="Research paper thumbnail of Re-examining machine translation metrics for paraphrase identification" 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" rel="nofollow" href="https://www.academia.edu/26513540/Re_examining_machine_translation_metrics_for_paraphrase_identification">Re-examining machine translation metrics for paraphrase identification</a></div><div class="wp-workCard_item"><span>Proceedings of the 2012 Conference of the North American Chapter of the Association For Computational Linguistics Human Language Technologies</span><span>, Jun 3, 2012</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract We propose to re-examine the hypothesis that automated metrics developed for MT evaluati...</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">Abstract We propose to re-examine the hypothesis that automated metrics developed for MT evaluation can prove useful for paraphrase identification in light of the significant work on the development of new MT metrics over the last 4 years. We show that a meta-classifier trained using nothing but recent MT metrics outperforms all previous paraphrase identification approaches on the Microsoft Research Paraphrase corpus. In addition, we apply our system to a second corpus developed for the task of plagiarism detection and ...</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="26513540"><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="26513540"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513540; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=26513540]").text(description); $(".js-view-count[data-work-id=26513540]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 26513540; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='26513540']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 26513540, 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" } } $('.js-work-strip[data-work-id=26513540]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":26513540,"title":"Re-examining machine translation metrics for paraphrase identification","translated_title":"","metadata":{"abstract":"Abstract We propose to re-examine the hypothesis that automated metrics developed for MT evaluation can prove useful for paraphrase identification in light of the significant work on the development of new MT metrics over the last 4 years. 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In addition, we apply our system to a second corpus developed for the task of plagiarism detection and ...","internal_url":"https://www.academia.edu/26513540/Re_examining_machine_translation_metrics_for_paraphrase_identification","translated_internal_url":"","created_at":"2016-06-26T11:24:37.496-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":50446196,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Re_examining_machine_translation_metrics_for_paraphrase_identification","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":50446196,"first_name":"Joel","middle_initials":null,"last_name":"Tetreault","page_name":"JoelTetreault","domain_name":"independent","created_at":"2016-06-25T07:02:03.031-07:00","display_name":"Joel Tetreault","url":"https://independent.academia.edu/JoelTetreault"},"attachments":[],"research_interests":[{"id":4696,"name":"Machine Translation","url":"https://www.academia.edu/Documents/in/Machine_Translation"}],"urls":[{"id":7253734,"url":"http://dl.acm.org/citation.cfm?id=2382055"}]}, 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="26513539"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/26513539/Rethinking_grammatical_error_annotation_and_evaluation_with_the_Amazon_Mechanical_Turk"><img alt="Research paper thumbnail of Rethinking grammatical error annotation and evaluation with the Amazon Mechanical Turk" 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" rel="nofollow" href="https://www.academia.edu/26513539/Rethinking_grammatical_error_annotation_and_evaluation_with_the_Amazon_Mechanical_Turk">Rethinking grammatical error annotation and evaluation with the Amazon Mechanical Turk</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract In this paper we present results from two pilot studies which show that using the Amazon...</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">Abstract In this paper we present results from two pilot studies which show that using the Amazon Mechanical Turk for preposition error annotation is as effective as using trained raters, but at a fraction of the time and cost. Based on these results, we propose a new evaluation method which makes it feasible to compare two error detection systems tested on different learner data sets.</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="26513539"><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="26513539"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513539; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=26513539]").text(description); $(".js-view-count[data-work-id=26513539]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 26513539; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='26513539']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 26513539, 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" } } $('.js-work-strip[data-work-id=26513539]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":26513539,"title":"Rethinking grammatical error annotation and evaluation with the Amazon Mechanical Turk","translated_title":"","metadata":{"abstract":"Abstract In this paper we present results from two pilot studies which show that using the Amazon Mechanical Turk for preposition error annotation is as effective as using trained raters, but at a fraction of the time and cost. Based on these results, we propose a new evaluation method which makes it feasible to compare two error detection systems tested on different learner data sets.","publication_date":{"day":null,"month":null,"year":2010,"errors":{}}},"translated_abstract":"Abstract In this paper we present results from two pilot studies which show that using the Amazon Mechanical Turk for preposition error annotation is as effective as using trained raters, but at a fraction of the time and cost. 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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="26513538"><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/26513538/Towards_using_structural_events_to_assess_non_native_speech"><img alt="Research paper thumbnail of Towards using structural events to assess non-native speech" class="work-thumbnail" src="https://attachments.academia-assets.com/46809878/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/26513538/Towards_using_structural_events_to_assess_non_native_speech">Towards using structural events to assess non-native speech</a></div><div class="wp-workCard_item"><span>Proceedings of the Naacl Hlt 2010 Fifth Workshop on Innovative Use of Nlp For Building Educational Applications</span><span>, Jun 5, 2010</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c4cbee7c5d2a9b0929bcd9d8948fd0ad" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":46809878,"asset_id":26513538,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/46809878/download_file?st=MTczMjQ2ODEyMSw4LjIyMi4yMDguMTQ2&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="26513538"><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="26513538"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513538; 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Using a set of transcribed audio files collected from the TOEFL Practice Test Online (TPO), we conducted a sophisticated annotation of structural events, including clause boundaries and types, as well as disfluencies. Based on words and the annotated structural events, we extracted features related to syntactic complexity, e.g., the mean length of clause (MLC) and dependent clause frequency (DEPC), and a feature related to disfluencies, the interruption point frequency per clause (IPC). Among these features, the IPC shows the highest correlation with holistic scores (r = −0.344). Furthermore, we increased the correlation with human scores by normalizing IPC by (1) MLC (r = −0.386), (2) DEPC (r = −0.429), and (3) both (r = −0.462). In this research, the features derived from structural events of speech transcriptions are found to predict holistic scores measuring speaking proficiency. This suggests that structural events estimated on speech word strings provide a potential way for assessing nonnative speech.","publication_date":{"day":5,"month":6,"year":2010,"errors":{}},"publication_name":"Proceedings of the Naacl Hlt 2010 Fifth Workshop on Innovative Use of Nlp For Building Educational Applications","grobid_abstract_attachment_id":46809878},"translated_abstract":null,"internal_url":"https://www.academia.edu/26513538/Towards_using_structural_events_to_assess_non_native_speech","translated_internal_url":"","created_at":"2016-06-26T11:24:36.993-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":50446196,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":46809878,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/46809878/thumbnails/1.jpg","file_name":"Towards_using_structural_events_to_asses20160626-13920-1obzq3h.pdf","download_url":"https://www.academia.edu/attachments/46809878/download_file?st=MTczMjQ2ODEyMSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Towards_using_structural_events_to_asses.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/46809878/Towards_using_structural_events_to_asses20160626-13920-1obzq3h-libre.pdf?1466965839=\u0026response-content-disposition=attachment%3B+filename%3DTowards_using_structural_events_to_asses.pdf\u0026Expires=1732459637\u0026Signature=OS-2e3sajNzmYIo0QuqgIbsSRLHxmo1odWzYWsUB-wVvfwfGq8Hv4NNCjzSwC46fEUxX5uCrVJZmTj1gx3I6SDfprjMx~lr1b1qELFWkSG8YG2D-JRan-AczpVxegkDqiHulS7vi22mjeEOC3J51p6ThGIPzt0zm-Aqp4oMC5r-YRMwx9TAKsETmLVEZVqshg-qRY~CZ4y6PPoFES2YBHZTnsJJEb9Jgxf9E6dgdIPQEVdMJlUkS0e5oaij4Wcg4~dIFA7-LYJofFBOAm9UzlP92X76OUtYjbpGvN~V-gaj8fPxigi0Am~bRJPgzG1baCMelnIDAADVJbEiklPevcA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Towards_using_structural_events_to_assess_non_native_speech","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":50446196,"first_name":"Joel","middle_initials":null,"last_name":"Tetreault","page_name":"JoelTetreault","domain_name":"independent","created_at":"2016-06-25T07:02:03.031-07:00","display_name":"Joel Tetreault","url":"https://independent.academia.edu/JoelTetreault"},"attachments":[{"id":46809878,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/46809878/thumbnails/1.jpg","file_name":"Towards_using_structural_events_to_asses20160626-13920-1obzq3h.pdf","download_url":"https://www.academia.edu/attachments/46809878/download_file?st=MTczMjQ2ODEyMSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Towards_using_structural_events_to_asses.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/46809878/Towards_using_structural_events_to_asses20160626-13920-1obzq3h-libre.pdf?1466965839=\u0026response-content-disposition=attachment%3B+filename%3DTowards_using_structural_events_to_asses.pdf\u0026Expires=1732459637\u0026Signature=OS-2e3sajNzmYIo0QuqgIbsSRLHxmo1odWzYWsUB-wVvfwfGq8Hv4NNCjzSwC46fEUxX5uCrVJZmTj1gx3I6SDfprjMx~lr1b1qELFWkSG8YG2D-JRan-AczpVxegkDqiHulS7vi22mjeEOC3J51p6ThGIPzt0zm-Aqp4oMC5r-YRMwx9TAKsETmLVEZVqshg-qRY~CZ4y6PPoFES2YBHZTnsJJEb9Jgxf9E6dgdIPQEVdMJlUkS0e5oaij4Wcg4~dIFA7-LYJofFBOAm9UzlP92X76OUtYjbpGvN~V-gaj8fPxigi0Am~bRJPgzG1baCMelnIDAADVJbEiklPevcA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[{"id":7253733,"url":"http://dl.acm.org/citation.cfm?id=1866805"}]}, 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="26513537"><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/26513537/Exploring_grammatical_error_correction_with_not_so_crummy_machine_translation"><img alt="Research paper thumbnail of Exploring grammatical error correction with not-so-crummy machine translation" class="work-thumbnail" src="https://attachments.academia-assets.com/46809877/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/26513537/Exploring_grammatical_error_correction_with_not_so_crummy_machine_translation">Exploring grammatical error correction with not-so-crummy machine translation</a></div><div class="wp-workCard_item"><span>Proceedings of the Seventh Workshop on Building Educational Applications Using Nlp</span><span>, Jun 7, 2012</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9f055bfc4cf0befc7c8dac070a6dba27" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":46809877,"asset_id":26513537,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/46809877/download_file?st=MTczMjQ2ODEyMSw4LjIyMi4yMDguMTQ2&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="26513537"><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="26513537"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513537; 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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="26513531"><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/26513531/Estimating_the_Reliability_of_MDP_Policies_a_Confidence_Interval_Approach"><img alt="Research paper thumbnail of Estimating the Reliability of MDP Policies: a Confidence Interval Approach" class="work-thumbnail" src="https://attachments.academia-assets.com/46809876/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/26513531/Estimating_the_Reliability_of_MDP_Policies_a_Confidence_Interval_Approach">Estimating the Reliability of MDP Policies: a Confidence Interval Approach</a></div><div class="wp-workCard_item"><span>Naacl</span><span>, 2007</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7b27d2027a4b304ca98e72627acd6b0e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":46809876,"asset_id":26513531,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/46809876/download_file?st=MTczMjQ2ODEyMSw4LjIyMi4yMDguMTQ2&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="26513531"><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="26513531"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513531; 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Resu...</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">We evaluate the effect of adding parse fea- tures to a leading model of preposition us- age. Results show a significant improve- ment in the preposition selection task on native speaker text and a modest increment in precision and recall in an ESL error de- tection task. Analysis of the parser output indicates that it is robust enough in the</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b943153eaac658dd14cfc81c1db847a2" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":46809820,"asset_id":26513528,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/46809820/download_file?st=MTczMjQ2ODEyMSw4LjIyMi4yMDguMTQ2&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="26513528"><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="26513528"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513528; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=26513528]").text(description); $(".js-view-count[data-work-id=26513528]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 26513528; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='26513528']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 26513528, 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: "b943153eaac658dd14cfc81c1db847a2" } } $('.js-work-strip[data-work-id=26513528]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":26513528,"title":"Using Parse Features for Preposition Selection and Error Detection","translated_title":"","metadata":{"abstract":"We evaluate the effect of adding parse fea- tures to a leading model of preposition us- age. 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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="26513527"><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/26513527/Using_an_Error_Annotated_Learner_Corpus_to_Develop_an_ESL_EFL_Error_Correction_System"><img alt="Research paper thumbnail of Using an Error-Annotated Learner Corpus to Develop an ESL/EFL Error Correction System" class="work-thumbnail" src="https://attachments.academia-assets.com/46809872/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/26513527/Using_an_Error_Annotated_Learner_Corpus_to_Develop_an_ESL_EFL_Error_Correction_System">Using an Error-Annotated Learner Corpus to Develop an ESL/EFL Error Correction System</a></div><div class="wp-workCard_item"><span>Language Resources and Evaluation</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents research on building a model of grammatical error correction, for preposition...</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">This paper presents research on building a model of grammatical error correction, for preposition errors in particular , in English text produced by language learners. Unlike most previous work which trains a statistical classifier exclusively on well-for med text written by native speakers, we train a classifier on a large-scale, error-tagged corpus of English essays written by EFL learners, relying</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="99c4b529c4fbcb51a3fa3b2bbef93c3d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":46809872,"asset_id":26513527,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/46809872/download_file?st=MTczMjQ2ODEyMSw4LjIyMi4yMDguMTQ2&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="26513527"><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="26513527"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513527; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=26513527]").text(description); $(".js-view-count[data-work-id=26513527]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 26513527; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='26513527']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 26513527, 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: "99c4b529c4fbcb51a3fa3b2bbef93c3d" } } $('.js-work-strip[data-work-id=26513527]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":26513527,"title":"Using an Error-Annotated Learner Corpus to Develop an ESL/EFL Error Correction System","translated_title":"","metadata":{"abstract":"This paper presents research on building a model of grammatical error correction, for preposition errors in particular , in English text produced by language learners. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="26513526"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/26513526/Dialogue_Structure_and_Pronoun_Resolution"><img alt="Research paper thumbnail of Dialogue Structure and Pronoun Resolution" class="work-thumbnail" src="https://attachments.academia-assets.com/46809818/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/26513526/Dialogue_Structure_and_Pronoun_Resolution">Dialogue Structure and Pronoun Resolution</a></div><div class="wp-workCard_item"><span>Discourse Anaphora and Anaphor Resolution Colloquium</span><span>, 2004</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents an empirical evaluation of a pronoun resolution algorithm augmented with disc...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper presents an empirical evaluation of a pronoun resolution algorithm augmented with discourse segmentation information. Past work has shown that segmenting discourse can aid in pronoun resolution by making potentially erroneous candidates inaccessible to a pronoun&#39;s search. 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$(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="26513525"><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/26513525/An_Empirical_Evaluation_of_Pronoun_Resolution_and_Clausal_Structure"><img alt="Research paper thumbnail of An Empirical Evaluation of Pronoun Resolution and Clausal Structure" class="work-thumbnail" src="https://attachments.academia-assets.com/46809816/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/26513525/An_Empirical_Evaluation_of_Pronoun_Resolution_and_Clausal_Structure">An Empirical Evaluation of Pronoun Resolution and Clausal Structure</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents an automated empiri- cal evaluation of the relationship between clausal struc...</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">This paper presents an automated empiri- cal evaluation of the relationship between clausal structure and pronominal refer- ence. Past work has theorized that in- corporating discourse structure can con- strain the search space in the resolution of pronouns since discourse segments, and thus potential antecedents, can be made inaccessible as the discourse pro- gresses and the focus changes. How- ever,</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8340f9bbec4491ca0620fcd041be748a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":46809816,"asset_id":26513525,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/46809816/download_file?st=MTczMjQ2ODEyMSw4LjIyMi4yMDguMTQ2&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="26513525"><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="26513525"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513525; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=26513525]").text(description); $(".js-view-count[data-work-id=26513525]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 26513525; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='26513525']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 26513525, 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: "8340f9bbec4491ca0620fcd041be748a" } } $('.js-work-strip[data-work-id=26513525]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":26513525,"title":"An Empirical Evaluation of Pronoun Resolution and Clausal Structure","translated_title":"","metadata":{"abstract":"This paper presents an automated empiri- cal evaluation of the relationship between clausal structure and pronominal refer- ence. 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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="26513524"><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/26513524/Incremental_Parsing_with_Reference_Interaction"><img alt="Research paper thumbnail of Incremental Parsing with Reference Interaction" class="work-thumbnail" src="https://attachments.academia-assets.com/46809870/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/26513524/Incremental_Parsing_with_Reference_Interaction">Incremental Parsing with Reference Interaction</a></div><div class="wp-workCard_item"><span>Meeting of the Association for Computational Linguistics</span><span>, 2000</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We present a general architecture for incremen- tal interaction between modules in a speech-to- i...</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">We present a general architecture for incremen- tal interaction between modules in a speech-to- intention continuous understanding dialogue sys- tem. This architecture is then instantiated in the form of an incremental parser which receives suit- ability feedback on NP constituents from a refer- ence resolution module. Oracle results indicate that perfect NP suitability judgments can provide a labelled-bracket error reduction</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="66e074e002357ba21b1893416c758c5f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":46809870,"asset_id":26513524,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/46809870/download_file?st=MTczMjQ2ODEyMSw4LjIyMi4yMDguMTQ2&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="26513524"><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="26513524"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513524; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=26513524]").text(description); $(".js-view-count[data-work-id=26513524]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 26513524; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='26513524']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 26513524, 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: "66e074e002357ba21b1893416c758c5f" } } $('.js-work-strip[data-work-id=26513524]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":26513524,"title":"Incremental Parsing with Reference Interaction","translated_title":"","metadata":{"abstract":"We present a general architecture for incremen- tal interaction between modules in a speech-to- intention continuous understanding dialogue sys- tem. 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While policy development is very important, choosing the best fea- tures to model the user state is equally im- portant since it impacts the</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c21b766579194477b99e9a22a5e5f153" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":46809873,"asset_id":26513523,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/46809873/download_file?st=MTczMjQ2ODEyMSw4LjIyMi4yMDguMTQ2&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="26513523"><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="26513523"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513523; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=26513523]").text(description); $(".js-view-count[data-work-id=26513523]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 26513523; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='26513523']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 26513523, 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: "c21b766579194477b99e9a22a5e5f153" } } $('.js-work-strip[data-work-id=26513523]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":26513523,"title":"Comparing the Utility of State Features in Spoken Dialogue Using Reinforcement Learning","translated_title":"","metadata":{"abstract":"Recent work in designing spoken dialogue systems has focused on using Reinforce- ment Learning to automatically learn the best action for a system to take at any point in the dialogue to maximize dia- logue success. 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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="26513522"><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/26513522/Using_Reinforcement_Learning_to_Build_a_Better_Model_of_Dialogue_State"><img alt="Research paper thumbnail of Using Reinforcement Learning to Build a Better Model of Dialogue State" class="work-thumbnail" src="https://attachments.academia-assets.com/46809869/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/26513522/Using_Reinforcement_Learning_to_Build_a_Better_Model_of_Dialogue_State">Using Reinforcement Learning to Build a Better Model of Dialogue State</a></div><div class="wp-workCard_item"><span>Eacl</span><span>, 2006</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="67bdf3e0baed6f4d0aa1f4506860fd98" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":46809869,"asset_id":26513522,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/46809869/download_file?st=MTczMjQ2ODEyMSw4LjIyMi4yMDguMTQ2&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="26513522"><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="26513522"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26513522; 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