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Conference Paper: CoSQL: A conversational text-to-SQL challenge towards cross-domain natural language interfaces to databases

TitleCoSQL: A conversational text-to-SQL challenge towards cross-domain natural language interfaces to databases
Authors
Issue Date2019
Citation
2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP 2019), Hong Kong, 3-7 November 2019. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019, p. 1962-1979 How to Cite?
AbstractWe present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems. It consists of 30k+ turns plus 10k+ annotated SQL queries, obtained from a Wizard-of-Oz (WOZ) collection of 3k dialogues querying 200 complex DBs spanning 138 domains. Each dialogue simulates a real-world DB query scenario with a crowd worker as a user exploring the DB and a SQL expert retrieving answers with SQL, clarifying ambiguous questions, or otherwise informing of unanswerable questions. When user questions are answerable by SQL, the expert describes the SQL and execution results to the user, hence maintaining a natural interaction flow. CoSQL introduces new challenges compared to existing task-oriented dialogue datasets: (1) the dialogue states are grounded in SQL, a domain-independent executable representation, instead of domain-specific slot-value pairs, and (2) because testing is done on unseen databases, success requires generalizing to new domains. CoSQL includes three tasks: SQL-grounded dialogue state tracking, response generation from query results, and user dialogue act prediction. We evaluate a set of strong baselines for each task and show that CoSQL presents significant challenges for future research. The dataset, baselines, and leaderboard will be released at https://yale-lily.github.io/cosql.
Persistent Identifierhttp://hdl.handle.net/10722/303669

 

DC FieldValueLanguage
dc.contributor.authorYu, Tao-
dc.contributor.authorZhang, Rui-
dc.contributor.authorEr, He Yang-
dc.contributor.authorLi, Suyi-
dc.contributor.authorXue, Eric-
dc.contributor.authorPang, Bo-
dc.contributor.authorLin, Xi Victoria-
dc.contributor.authorTan, Yi Chern-
dc.contributor.authorShi, Tianze-
dc.contributor.authorLi, Zihan-
dc.contributor.authorJiang, Youxuan-
dc.contributor.authorYasunaga, Michihiro-
dc.contributor.authorShim, Sungrok-
dc.contributor.authorChen, Tao-
dc.contributor.authorFabbri, Alexander-
dc.contributor.authorLi, Zifan-
dc.contributor.authorChen, Luyao-
dc.contributor.authorZhang, Yuwen-
dc.contributor.authorDixit, Shreya-
dc.contributor.authorZhang, Vincent-
dc.contributor.authorXiong, Caiming-
dc.contributor.authorSocher, Richard-
dc.contributor.authorLasecki, Walter S.-
dc.contributor.authorRadev, Dragomir-
dc.date.accessioned2021-09-15T08:25:47Z-
dc.date.available2021-09-15T08:25:47Z-
dc.date.issued2019-
dc.identifier.citation2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP 2019), Hong Kong, 3-7 November 2019. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019, p. 1962-1979-
dc.identifier.urihttp://hdl.handle.net/10722/303669-
dc.description.abstractWe present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems. It consists of 30k+ turns plus 10k+ annotated SQL queries, obtained from a Wizard-of-Oz (WOZ) collection of 3k dialogues querying 200 complex DBs spanning 138 domains. Each dialogue simulates a real-world DB query scenario with a crowd worker as a user exploring the DB and a SQL expert retrieving answers with SQL, clarifying ambiguous questions, or otherwise informing of unanswerable questions. When user questions are answerable by SQL, the expert describes the SQL and execution results to the user, hence maintaining a natural interaction flow. CoSQL introduces new challenges compared to existing task-oriented dialogue datasets: (1) the dialogue states are grounded in SQL, a domain-independent executable representation, instead of domain-specific slot-value pairs, and (2) because testing is done on unseen databases, success requires generalizing to new domains. CoSQL includes three tasks: SQL-grounded dialogue state tracking, response generation from query results, and user dialogue act prediction. We evaluate a set of strong baselines for each task and show that CoSQL presents significant challenges for future research. The dataset, baselines, and leaderboard will be released at https://yale-lily.github.io/cosql.-
dc.languageeng-
dc.relation.ispartofProceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleCoSQL: A conversational text-to-SQL challenge towards cross-domain natural language interfaces to databases-
dc.typeConference_Paper-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.18653/v1/D19-1204-
dc.identifier.scopuseid_2-s2.0-85084321905-
dc.identifier.spage1962-
dc.identifier.epage1979-

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