Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 3063–3074 August 1–6, 2021. ©2021 Association for Computational Linguistics 3063 Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL Zhi Chen, Lu Chen∗, Hanqi Li, Ruisheng Cao, Da Ma, Mengyue Wu and Kai Yu∗ X-LANCE Lab, Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University Shanghai Jiao Tong University, Shanghai, China State Key Lab of Media Convergence Production Technology and Systems, Beijing, China {zhenchi713, chenlusz, kai.yu}@sjtu.edu.cn Abstract Recently, Text-to-SQL for multi-turn dialogue has attracted great interest. Here, the user input of the current turn is parsed into the corresponding SQL query of the appropriate database, given all previous dialogue history. Current approaches mostly employ end-to-end models and consequently face two challenges. First, dialogue history modeling and Text-to- SQL parsing are implicitly combined, hence it is hard to carry out interpretable analysis and obtain targeted improvement. Second, SQL annotation of multi-turn dialogue is very expensive, leading to training data sparsity. In this paper, we propose a novel decoupled multi-turn Text-to-SQL framework, where an utterance rewrite model first explicitly solves completion of dialogue context, and then a single-turn Text-to-SQL parser follows. dual learning approach is also proposed forA the utterance rewrite model to address the data sparsity problem. Compared with end-to-end approaches, the proposed decoupled method can achieve excellent performance without any annotated in-domain data. With just a few an- notated rewrite cases, the decoupled method outperforms the released state-of-the-art end- to-end models on both SParC and CoSQL datasets. 1 Introduction Text-to-SQL has lately become an interesting re- search topic along with the high demand to query a database using natural language (NL). Standard large database format can only be accessed with Structured Query Language (SQL), which requires certain special knowledge from users, hence low- ering the accessibility of these databases. Text-to- SQL tasks, however, greatly minimize this gap and allow the query based on NL. Previous work on ∗The corresponding authors are Lu Chen and Kai Yu. Show the treatment details. Utterances Semantic-Completion Utterances Show the treatment details. Show the treatment details ordered the cost in ascending order. Order the cost in ascending order . Ellipsis Show the treatment details ordered the cost in descending order. What about in descending order? SELECT cost_of_treatment FROM Treatments ORDER BY cost_of_treatment DESC Ellipsis Which treatment is the most recent cost? Which one is the most recent cost? SELECT cost_of_treatment FROM Treatments ORDER BY date_of_treatment DESC LIMIT 1 Co-reference SELECT cost_of_treatment FROM Treatments ORDER BY cost_of_treatment ASC SELECT * FROM Treatments Figure 1: An example to demonstrate the co-reference and ellipsis phenomenon in a conversation, where the right column shows the annotated semantic-completion utterances. Text-to-SQL mostly focuses on single-turn utter- ance inference, evaluated on context-independent Text-to-SQL benchmarks. Nevertheless, in prac- tice, the users usually need to interact with the Text-to-SQL system step-by-step to address their query purpose clearly. Under such conversation scenarios, the co-reference and information ellipses are always present, shown in Figure 1. Recently proposed …

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