Suyi Li


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CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
Tao Yu | Rui Zhang | Heyang Er | Suyi Li | Eric Xue | Bo Pang | Xi Victoria Lin | Yi Chern Tan | Tianze Shi | Zihan Li | Youxuan Jiang | Michihiro Yasunaga | Sungrok Shim | Tao Chen | Alexander Fabbri | Zifan Li | Luyao Chen | Yuwen Zhang | Shreya Dixit | Vincent Zhang | Caiming Xiong | Richard Socher | Walter Lasecki | Dragomir Radev
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We 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

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Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model
Alexander Fabbri | Irene Li | Tianwei She | Suyi Li | Dragomir Radev
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Automatic generation of summaries from multiple news articles is a valuable tool as the number of online publications grows rapidly. Single document summarization (SDS) systems have benefited from advances in neural encoder-decoder model thanks to the availability of large datasets. However, multi-document summarization (MDS) of news articles has been limited to datasets of a couple of hundred examples. In this paper, we introduce Multi-News, the first large-scale MDS news dataset. Additionally, we propose an end-to-end model which incorporates a traditional extractive summarization model with a standard SDS model and achieves competitive results on MDS datasets. We benchmark several methods on Multi-News and hope that this work will promote advances in summarization in the multi-document setting.

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SParC: Cross-Domain Semantic Parsing in Context
Tao Yu | Rui Zhang | Michihiro Yasunaga | Yi Chern Tan | Xi Victoria Lin | Suyi Li | Heyang Er | Irene Li | Bo Pang | Tao Chen | Emily Ji | Shreya Dixit | David Proctor | Sungrok Shim | Jonathan Kraft | Vincent Zhang | Caiming Xiong | Richard Socher | Dragomir Radev
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We present SParC, a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences (12k+ individual questions annotated with SQL queries). It is obtained from controlled user interactions with 200 complex databases over 138 domains. We provide an in-depth analysis of SParC and show that it introduces new challenges compared to existing datasets. SParC demonstrates complex contextual dependencies, (2) has greater semantic diversity, and (3) requires generalization to unseen domains due to its cross-domain nature and the unseen databases at test time. We experiment with two state-of-the-art text-to-SQL models adapted to the context-dependent, cross-domain setup. The best model obtains an exact match accuracy of 20.2% over all questions and less than10% over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research. The dataset, baselines, and leaderboard are released at