CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parsers

• URL: http://arxiv.org/abs/2205.07686v2
• Date: Tue, 17 May 2022 15:44:21 GMT
• Title: CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parsers
• Authors: Dongling Xiao, Linzheng Chai, Qian-Wen Zhang, Zhao Yan, Zhoujun Li, Yunbo Cao
• Abstract summary: Context-dependent text-to-reference is the task of translating multi-turn questions into database-related queries. In this paper, we propose CQR-couple, which uses auxiliary Conversational Question Reformulation (CQR) learning to explicitly exploit and decouple contextual dependency forsql parsing. At the time of writing, our CQR-couple achieves new state-of-the-art results on two context-dependent benchmarks SParC and Co.
• Score: 35.36754559708944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Context-dependent text-to-SQL is the task of translating multi-turn questions into database-related SQL queries. Existing methods typically focus on making full use of history context or previously predicted SQL for currently SQL parsing, while neglecting to explicitly comprehend the schema and conversational dependency, such as co-reference, ellipsis and user focus change. In this paper, we propose CQR-SQL, which uses auxiliary Conversational Question Reformulation (CQR) learning to explicitly exploit schema and decouple contextual dependency for SQL parsing. Specifically, we first present a schema enhanced recursive CQR method to produce domain-relevant self-contained questions. Secondly, we train CQR-SQL models to map the semantics of multi-turn questions and auxiliary self-contained questions into the same latent space through schema grounding consistency task and tree-structured SQL parsing consistency task, which enhances the abilities of SQL parsing by adequately contextual understanding. At the time of writing, our CQR-SQL achieves new state-of-the-art results on two context-dependent text-to-SQL benchmarks SParC and CoSQL.

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