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<!DOCTYPE html> <html > <head> <meta charset="utf-8"> <meta rel="search" type="application/opensearchdescription+xml" href="/open_search.xml" title="Academia.edu"> <meta content="width=device-width, initial-scale=1" name="viewport"> <meta name="google-site-verification" content="bKJMBZA7E43xhDOopFZkssMMkBRjvYERV-NaN4R6mrs"> <meta name="csrf-param" content="authenticity_token" /> <meta name="csrf-token" content="rTNZBj5+PXVhtkNb/ErpAPsD20XWyqxgMqaVkQs2K0zD4AjO/txkl+BPSljkqMSFl8JGH+UDgE7/7ahkjV/+AA==" /> <meta name="citation_title" content="A Survey on Dialog Management: Recent Advances and Challenges" /> <meta name="citation_publication_date" content="2020/01/01" /> <meta name="citation_journal_title" content="ArXiv" /> <meta name="citation_author" content="chengguang tang" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:url" content="https://www.academia.edu/64391635/A_Survey_on_Dialog_Management_Recent_Advances_and_Challenges" /> <meta name="twitter:title" content="A Survey on Dialog Management: Recent Advances and Challenges" /> <meta name="twitter:description" content="Dialog management (DM) is a crucial component in a task-oriented dialog system. Given the dialog history, DM predicts the dialog state and decides the next action that the dialog agent should take. Recently, dialog policy learning has been widely" /> <meta name="twitter:image" content="https://0.academia-photos.com/164963742/70220139/58634006/s200_chengguang.tang.jpeg" /> <meta property="fb:app_id" content="2369844204" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.academia.edu/64391635/A_Survey_on_Dialog_Management_Recent_Advances_and_Challenges" /> <meta property="og:title" content="A Survey on Dialog Management: Recent Advances and Challenges" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif" /> <meta property="og:description" content="Dialog management (DM) is a crucial component in a task-oriented dialog system. Given the dialog history, DM predicts the dialog state and decides the next action that the dialog agent should take. Recently, dialog policy learning has been widely" /> <meta property="article:author" content="https://independent.academia.edu/chengguangtang" /> <meta name="description" content="Dialog management (DM) is a crucial component in a task-oriented dialog system. Given the dialog history, DM predicts the dialog state and decides the next action that the dialog agent should take. Recently, dialog policy learning has been widely" /> <title>(PDF) A Survey on Dialog Management: Recent Advances and Challenges | chengguang tang - Academia.edu</title> <link rel="canonical" href="https://www.academia.edu/64391635/A_Survey_on_Dialog_Management_Recent_Advances_and_Challenges" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = '49879c2402910372f4abc62630a427bbe033d190'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1732456818000); window.Aedu.timeDifference = new Date().getTime() - 1732456818000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"Dialog management (DM) is a crucial component in a task-oriented dialog system. Given the dialog history, DM predicts the dialog state and decides the next action that the dialog agent should take. Recently, dialog policy learning has been widely formulated as a Reinforcement Learning (RL) problem, and more works focus on the applicability of DM. In this paper, we survey recent advances and challenges within three critical topics for DM: (1) improving model scalability to facilitate dialog system modeling in new scenarios, (2) dealing with the data scarcity problem for dialog policy learning, and (3) enhancing the training efficiency to achieve better task-completion performance . We believe that this survey can shed a light on future research in dialog management.","author":[{"@context":"https://schema.org","@type":"Person","name":"chengguang tang"}],"contributor":[],"dateCreated":"2021-12-15","dateModified":null,"datePublished":"2020-01-01","headline":"A Survey on Dialog Management: Recent Advances and Challenges","inLanguage":"en","keywords":["Computer Science","arXiv"],"locationCreated":null,"publication":"ArXiv","publisher":{"@context":"https://schema.org","@type":"Organization","name":"ArXiv"},"image":null,"thumbnailUrl":null,"url":"https://www.academia.edu/64391635/A_Survey_on_Dialog_Management_Recent_Advances_and_Challenges","sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":null}]}</script><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/single_work_page/loswp-352e32ba4e89304dc0b4fa5b3952eef2198174c54cdb79066bc62e91c68a1a91.css" /><link rel="stylesheet" media="all" 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Given the dialog history, DM predicts the dialog state and decides the next action that the dialog agent should take. Recently, dialog policy learning has been widely formulated as a Reinforcement Learning (RL) problem, and more works focus on the applicability of DM. In this paper, we survey recent advances and challenges within three critical topics for DM: (1) improving model scalability to facilitate dialog system modeling in new scenarios, (2) dealing with the data scarcity problem for dialog policy learning, and (3) enhancing the training efficiency to achieve better task-completion performance . We believe that this survey can shed a light on future research in dialog management.","publisher":"ArXiv","publication_date":"2020,,","publication_name":"ArXiv"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"A Survey on Dialog Management: Recent Advances and Challenges","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [164963742]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loswp.appleClientId = 'edu.academia.applesignon';</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":76449794,"attachmentType":"pdf"}"><img alt="First page of “A Survey on Dialog Management: Recent Advances and Challenges”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/76449794/mini_magick20211215-5900-19ogpsq.png?1639623235" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/assets/single_work_splash/adobe.icon-574afd46eb6b03a77a153a647fb47e30546f9215c0ee6a25df597a779717f9ef.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">A Survey on Dialog Management: Recent Advances and Challenges</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="164963742" href="https://independent.academia.edu/chengguangtang"><img alt="Profile image of chengguang tang" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/164963742/70220139/58634006/s65_chengguang.tang.jpeg" />chengguang tang</a></div><p class="ds-work-card--detail ds2-5-body-sm">2020, ArXiv</p><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">Dialog management (DM) is a crucial component in a task-oriented dialog system. Given the dialog history, DM predicts the dialog state and decides the next action that the dialog agent should take. Recently, dialog policy learning has been widely formulated as a Reinforcement Learning (RL) problem, and more works focus on the applicability of DM. In this paper, we survey recent advances and challenges within three critical topics for DM: (1) improving model scalability to facilitate dialog system modeling in new scenarios, (2) dealing with the data scarcity problem for dialog policy learning, and (3) enhancing the training efficiency to achieve better task-completion performance . We believe that this survey can shed a light on future research in dialog management.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":76449794,"attachmentType":"pdf","workUrl":"https://www.academia.edu/64391635/A_Survey_on_Dialog_Management_Recent_Advances_and_Challenges"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":76449794,"attachmentType":"pdf","workUrl":"https://www.academia.edu/64391635/A_Survey_on_Dialog_Management_Recent_Advances_and_Challenges"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div></div><div data-auto_select="false" data-client_id="331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b" data-doc_id="76449794" data-landing_url="https://www.academia.edu/64391635/A_Survey_on_Dialog_Management_Recent_Advances_and_Challenges" data-login_uri="https://www.academia.edu/registrations/google_one_tap" data-moment_callback="onGoogleOneTapEvent" id="g_id_onload"></div><div class="ds-top-related-works--grid-container"><div class="ds-related-content--container ds-top-related-works--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="0" data-entity-id="30524414" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/30524414/Optimizing_dialogue_management_with_reinforcement_learning_Experiments_">Optimizing dialogue management with reinforcement learning: Experiments …</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="34843701" href="https://independent.academia.edu/MarilynWalker5">Marilyn Walker</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Artificial …</p><p class="ds-related-work--abstract ds2-5-body-sm">Designing the dialogue policy of a spoken dialogue system involves many nontrivial choices. This paper presents a reinforcement learning approach for automatically optimiz- ing a dialogue policy, which addresses the technical challenges in applying reinforcement ...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Optimizing dialogue management with reinforcement learning: Experiments …","attachmentId":50968493,"attachmentType":"pdf","work_url":"https://www.academia.edu/30524414/Optimizing_dialogue_management_with_reinforcement_learning_Experiments_","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/30524414/Optimizing_dialogue_management_with_reinforcement_learning_Experiments_"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="64024556" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/64024556/Dialog_policy_optimization_for_low_resource_setting_using_Self_play_and_Reward_based_Sampling">Dialog policy optimization for low resource setting using Self-play and Reward based Sampling</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="61028408" href="https://independent.academia.edu/durashilangappuli">durashi langappuli</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2020</p><p class="ds-related-work--abstract ds2-5-body-sm">Reinforcement Learning is considered as the state of the art approach for dialogue policy optimization in task-oriented dialogue systems. However, these models demand a large corpus of dialogues to learn effectively. Training Reinforcement Learning agent with low data amount tends to overfit the agent. Although synthesizing dialogue agendas with dialogue Self-play using rule-based agents and crowdsourcing has demonstrated promising results with the low amount of samples, these methods hold limitations. For instance, rulebased agents acquire specific domain and language while crowdsourcing demands a high price and domain experts, especially in local languages. In this paper, we address these limitations by proposing a novel approach for synthetic agenda generation by acknowledging the underlying probability distribution of the user agendas and a reward-based sampling method that prioritizes failed dialogue acts. Evaluations conducted shows leveraged performance without overfitting, c...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Dialog policy optimization for low resource setting using Self-play and Reward based Sampling","attachmentId":76253808,"attachmentType":"pdf","work_url":"https://www.academia.edu/64024556/Dialog_policy_optimization_for_low_resource_setting_using_Self_play_and_Reward_based_Sampling","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/64024556/Dialog_policy_optimization_for_low_resource_setting_using_Self_play_and_Reward_based_Sampling"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="92048809" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/92048809/Predictable_and_Adaptive_Goal_oriented_Dialog_Policy_Generation">Predictable and Adaptive Goal-oriented Dialog Policy Generation</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="248226883" href="https://independent.academia.edu/NhatLe161">Nhat Le</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2021 IEEE 15th International Conference on Semantic Computing (ICSC)</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Predictable and Adaptive Goal-oriented Dialog Policy Generation","attachmentId":95163659,"attachmentType":"pdf","work_url":"https://www.academia.edu/92048809/Predictable_and_Adaptive_Goal_oriented_Dialog_Policy_Generation","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/92048809/Predictable_and_Adaptive_Goal_oriented_Dialog_Policy_Generation"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="110026802" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/110026802/Deep_Reinforcement_Learning_for_Dialogue_Systems_with_Dynamic_User_Goals">Deep Reinforcement Learning for Dialogue Systems with Dynamic User Goals</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="166390836" href="https://retired.academia.edu/glenchandler">glen chandler</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2020</p><p class="ds-related-work--abstract ds2-5-body-sm">Dialogue systems have recently become a widely used system across the world. 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We ...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Deep Reinforcement Learning for Dialogue Systems with Dynamic User Goals","attachmentId":107972837,"attachmentType":"pdf","work_url":"https://www.academia.edu/110026802/Deep_Reinforcement_Learning_for_Dialogue_Systems_with_Dynamic_User_Goals","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/110026802/Deep_Reinforcement_Learning_for_Dialogue_Systems_with_Dynamic_User_Goals"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="121528854" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/121528854/Conversation_Learner_A_Machine_Teaching_Tool_for_Building_Dialog_Managers_for_Task_Oriented_Dialog_Systems">Conversation Learner - A Machine Teaching Tool for Building Dialog Managers for Task-Oriented Dialog Systems</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="310442875" href="https://independent.academia.edu/LarsLiden">Lars Liden</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 2020</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Conversation Learner - A Machine Teaching Tool for Building Dialog Managers for Task-Oriented Dialog Systems","attachmentId":116380144,"attachmentType":"pdf","work_url":"https://www.academia.edu/121528854/Conversation_Learner_A_Machine_Teaching_Tool_for_Building_Dialog_Managers_for_Task_Oriented_Dialog_Systems","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/121528854/Conversation_Learner_A_Machine_Teaching_Tool_for_Building_Dialog_Managers_for_Task_Oriented_Dialog_Systems"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="91518249" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/91518249/SUMBT_LaRL_End_to_end_Neural_Task_oriented_Dialog_System_with_Reinforcement_Learning">SUMBT+LaRL: End-to-end Neural Task-oriented Dialog System with Reinforcement Learning</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="72898483" href="https://independent.academia.edu/SeokhwanJo">Seokhwan Jo</a></div><p class="ds-related-work--metadata ds2-5-body-xs">ArXiv, 2020</p><p class="ds-related-work--abstract ds2-5-body-sm">The recent advent of neural approaches for developing each dialog component in task-oriented dialog systems has remarkably improved, yet optimizing the overall system performance remains a challenge. In this paper, we propose an end-to-end trainable neural dialog system with reinforcement learning, named SUMBT+LaRL. The SUMBT+ estimates user-acts as well as dialog belief states, and the LaRL models latent system action spaces and generates responses given the estimated contexts. We experimentally demonstrate that the training framework in which the SUMBT+ and LaRL are separately pretrained and then the entire system is fine-tuned significantly increases dialog success rates. We propose new success criteria for reinforcement learning to the end-to-end dialog system as well as provide experimental analysis on a different result aspect depending on the success criteria and evaluation methods. Consequently, our model achieved the new state-of-the-art success rate of 85.4% on corpus-base...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"SUMBT+LaRL: End-to-end Neural Task-oriented Dialog System with Reinforcement Learning","attachmentId":94783550,"attachmentType":"pdf","work_url":"https://www.academia.edu/91518249/SUMBT_LaRL_End_to_end_Neural_Task_oriented_Dialog_System_with_Reinforcement_Learning","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/91518249/SUMBT_LaRL_End_to_end_Neural_Task_oriented_Dialog_System_with_Reinforcement_Learning"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="26513438" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/26513438/Using_reinforcement_learning_to_build_a_better_model_of_dialogue_state">Using reinforcement learning to build a better model of dialogue state</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="50446196" href="https://independent.academia.edu/JoelTetreault">Joel Tetreault</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2006</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Using reinforcement learning to build a better model of dialogue state","attachmentId":46809839,"attachmentType":"pdf","work_url":"https://www.academia.edu/26513438/Using_reinforcement_learning_to_build_a_better_model_of_dialogue_state","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/26513438/Using_reinforcement_learning_to_build_a_better_model_of_dialogue_state"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="14976651" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/14976651/Hybrid_Reinforcement_Supervised_Learning_of_Dialogue_Policies_from_Fixed_Data_Sets">Hybrid Reinforcement/Supervised Learning of Dialogue Policies from Fixed Data Sets</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="33971246" href="https://independent.academia.edu/JamesHenderson28">James Henderson</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Computational Linguistics, 2008</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Hybrid Reinforcement/Supervised Learning of Dialogue Policies from Fixed Data Sets","attachmentId":43681476,"attachmentType":"pdf","work_url":"https://www.academia.edu/14976651/Hybrid_Reinforcement_Supervised_Learning_of_Dialogue_Policies_from_Fixed_Data_Sets","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/14976651/Hybrid_Reinforcement_Supervised_Learning_of_Dialogue_Policies_from_Fixed_Data_Sets"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="79321656" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/79321656/A_Survey_on_Reinforcement_Learning_for_Dialogue_Systems">A Survey on Reinforcement Learning for Dialogue Systems</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="191316872" href="https://independent.academia.edu/AmanSoni105">Aman Soni</a></div><p class="ds-related-work--metadata ds2-5-body-xs">viXra, 2019</p><p class="ds-related-work--abstract ds2-5-body-sm">Dialogue systems are computer systems which com- municate with humans using natural language. The goal is not just to imitate human communication but to learn from these interactions and improve the system’s behaviour over time. Therefore, different machine learning approaches can be implemented with Reinforcement Learning being one of the most promising techniques to generate a contextually and semantically appropriate response. This paper outlines the current state-of- the-art methods and algorithms for integration of Reinforcement Learning techniques into dialogue systems.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Survey on Reinforcement Learning for Dialogue Systems","attachmentId":86075644,"attachmentType":"pdf","work_url":"https://www.academia.edu/79321656/A_Survey_on_Reinforcement_Learning_for_Dialogue_Systems","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/79321656/A_Survey_on_Reinforcement_Learning_for_Dialogue_Systems"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="76839136" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/76839136/Reinforcement_Learning_With_Simulated_User_For_Automatic_Dialog_Strategy_Optimization">Reinforcement Learning With Simulated User For Automatic Dialog Strategy Optimization</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="129160128" href="https://ugh.academia.edu/MinhQuangNguyen">Minh Quang Nguyen</a></div><p class="ds-related-work--abstract ds2-5-body-sm">In this paper, we propose a solution to the problem of formulating strategies for a spoken dialog system. Our approach is based on reinforcement learning with the help of a simulated user in order to identify an optimal dialog strategy. Our method considers the Markov decision process to be a framework for representation of speech dialog in which the states represent history and discourse context, the actions are dialog acts and the transition strategies are decisions on actions to take between states. We present our reinforcement learning architecture with a novel objective function that is based on dialog quality rather than its duration.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Reinforcement Learning With Simulated User For Automatic Dialog Strategy Optimization","attachmentId":84413805,"attachmentType":"pdf","work_url":"https://www.academia.edu/76839136/Reinforcement_Learning_With_Simulated_User_For_Automatic_Dialog_Strategy_Optimization","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/76839136/Reinforcement_Learning_With_Simulated_User_For_Automatic_Dialog_Strategy_Optimization"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":76449794,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":76449794,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_76449794" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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