MIGA: A Unified Multi-task Generation Framework for Conversational Text-to-SQL

• URL: http://arxiv.org/abs/2212.09278v1
• Date: Mon, 19 Dec 2022 07:14:32 GMT
• Title: MIGA: A Unified Multi-task Generation Framework for Conversational Text-to-SQL
• Authors: Yingwen Fu, Wenjie Ou, Zhou Yu, and Yue Lin
• Abstract summary: Most state-of-the-art conversational text-to-generative methods are incompatible with pre-trained language models (PLMs), such as T5. We present a two-stage unified MultI-task Generation frAmeme (MIGA) that leverages PLMs' ability to tackle conversational text-to-work.
• Score: 48.34333725045152
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conversational text-to-SQL is designed to translate multi-turn natural language questions into their corresponding SQL queries. Most state-of-the-art conversational text- to-SQL methods are incompatible with generative pre-trained language models (PLMs), such as T5. In this paper, we present a two-stage unified MultI-task Generation frAmework (MIGA) that leverages PLMs' ability to tackle conversational text-to-SQL. In the pre-training stage, MIGA first decomposes the main task into several related sub-tasks and then unifies them into the same sequence-to-sequence (Seq2Seq) paradigm with task-specific natural language prompts to boost the main task from multi-task training. Later in the fine-tuning stage, we propose four SQL perturbations to alleviate the error propagation problem. MIGA tends to achieve state-of-the-art performance on two benchmarks (SparC and CoSQL). We also provide extensive analyses and discussions to shed light on some new perspectives for conversational text-to-SQL.

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