QURG: Question Rewriting Guided Context-Dependent Text-to-SQL Semantic Parsing

• URL: http://arxiv.org/abs/2305.06655v2
• Date: Tue, 16 May 2023 06:22:47 GMT
• Title: QURG: Question Rewriting Guided Context-Dependent Text-to-SQL Semantic Parsing
• Authors: Linzheng Chai, Dongling Xiao, Jian Yang, Liqun Yang, Qian-Wen Zhang, Yunbo Cao, Zhoujun Li, Zhao Yan
• Abstract summary: This paper presents QURG, a novel Question Rewriting Guided approach to help the models achieve adequate contextual understanding. We first train a question rewriting model to complete the current question based on question context, and convert them into a rewriting edit matrix. We further design a two-stream matrix encoder to jointly model rewriting relations between question and context, and the schema linking relations between natural language and structured schema.
• Score: 46.05006486399823
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Context-dependent Text-to-SQL aims to translate multi-turn natural language questions into SQL queries. Despite various methods have exploited context-dependence information implicitly for contextual SQL parsing, there are few attempts to explicitly address the dependencies between current question and question context. This paper presents QURG, a novel Question Rewriting Guided approach to help the models achieve adequate contextual understanding. Specifically, we first train a question rewriting model to complete the current question based on question context, and convert them into a rewriting edit matrix. We further design a two-stream matrix encoder to jointly model the rewriting relations between question and context, and the schema linking relations between natural language and structured schema. Experimental results show that QURG significantly improves the performances on two large-scale context-dependent datasets SParC and CoSQL, especially for hard and long-turn questions.

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