{"id":569514,"date":"2019-02-24T10:44:56","date_gmt":"2019-02-24T18:44:56","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&p=569514"},"modified":"2019-06-17T13:49:02","modified_gmt":"2019-06-17T20:49:02","slug":"multi-domain-task-completion-dialog-challenge","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/multi-domain-task-completion-dialog-challenge\/","title":{"rendered":"Multi-Domain Task-Completion Dialog Challenge"},"content":{"rendered":"

As part of the Eighth Dialog System Technology Challenge (DSTC8<\/a>), Microsoft Research and Tsinghua University are hosting a track intended to foster progress in two<\/strong> important aspects of dialog systems: dialog complexity and scaling to new domains. For this DSTC8 track, there are two<\/strong> tasks you can compete in (see below). The challenge runs from\u00a0June 17, 2019 – October 6, 2019.<\/strong><\/p>\n

 <\/p>\n

Task 1<\/strong> –\u00a0 There is increasing interest in building complex bots that span over multiple sub-domains to accomplish a complex user goal such as travel planning. Travel planning may include sub-domains like hotels, restaurants, tourist attractions, and so on. To advance state-of-the-art technologies for handling complex dialogs, we offer a timely task focusing on multi-domain end-to-end task completion dialog.<\/p>\n

Sign up to participate in Task 1 at https:\/\/aka.ms\/dstc8-task1<\/a>.<\/p>\n

 <\/p>\n

Task 2<\/strong> – Neural dialog systems require very large datasets to learn how to output consistent and grammatically-correct sentences. This need for large datasets makes it extremely hard to scale out the system to new domains with limited in-domain data. With Task 2, our goal is to investigate whether sample complexity can decrease with time. In other words, the goal of Task 2 is to investigate whether a dialog system that was trained on a large corpus can learn to converse about a new domain given a much smaller in-domain corpus.<\/p>\n

Sign up to participate in Task 2 at https:\/\/aka.ms\/dstc8-task2<\/a>.<\/p>\n

Check out the MetaLWOz dataset<\/a> that is used for Task 2.<\/p>\n","protected":false},"excerpt":{"rendered":"

As part of the Eighth Dialog System Technology Challenge (DSTC8), Microsoft Research and Tsinghua University are hosting a track intended to foster progress in two important aspects of dialog systems: dialog complexity and scaling to new domains. For this DSTC8 track, there are two tasks you can compete in (see below). The challenge runs from\u00a0June […]<\/p>\n","protected":false},"featured_media":592771,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13556,13545,13554],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-569514","msr-project","type-msr-project","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-research-area-human-language-technologies","msr-research-area-human-computer-interaction","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"","related-publications":[575775,579838,645273],"related-downloads":[],"related-videos":[],"related-groups":[629145],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[{"type":"user_nicename","display_name":"Hannes Schulz","user_id":37188,"people_section":"Section name 1","alias":"haschulz"},{"type":"user_nicename","display_name":"Adam Atkinson","user_id":37095,"people_section":"Section name 1","alias":"adatkins"},{"type":"user_nicename","display_name":"Jianfeng Gao","user_id":32246,"people_section":"Section name 1","alias":"jfgao"},{"type":"user_nicename","display_name":"Mahmoud Adada","user_id":37176,"people_section":"Section name 1","alias":"maadada"},{"type":"guest","display_name":"Minlie Huang","user_id":569517,"people_section":"Section name 1","alias":""},{"type":"user_nicename","display_name":"Shikhar Sharma","user_id":36557,"people_section":"Section name 1","alias":"shsh"}],"msr_research_lab":[437514],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/569514"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":6,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/569514\/revisions"}],"predecessor-version":[{"id":593623,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/569514\/revisions\/593623"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/592771"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=569514"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=569514"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=569514"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=569514"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=569514"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}