Pay More Attention to History: A Context Modeling Strategy for Conversational Text-to-SQL

• URL: http://arxiv.org/abs/2112.08735v1
• Date: Thu, 16 Dec 2021 09:41:04 GMT
• Title: Pay More Attention to History: A Context Modeling Strategy for Conversational Text-to-SQL
• Authors: Yuntao Li, Hanchu Zhang, Yutian Li, Sirui Wang, Wei Wu, Yan Zhang
• Abstract summary: One of the most intractable problem of conversational text-to- domain is modeling the semantics of multi-turn queries. This paper shows that explicit modeling the semantic changes by adding each turn and the summarization of the whole context can bring better performance.
• Score: 8.038535788630542
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conversational text-to-SQL aims at converting multi-turn natural language queries into their corresponding SQL representations. One of the most intractable problem of conversational text-to-SQL is modeling the semantics of multi-turn queries and gathering proper information required for the current query. This paper shows that explicit modeling the semantic changes by adding each turn and the summarization of the whole context can bring better performance on converting conversational queries into SQLs. In particular, we propose two conversational modeling tasks in both turn grain and conversation grain. These two tasks simply work as auxiliary training tasks to help with multi-turn conversational semantic parsing. We conducted empirical studies and achieve new state-of-the-art results on large-scale open-domain conversational text-to-SQL dataset. The results demonstrate that the proposed mechanism significantly improves the performance of multi-turn semantic parsing.

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