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

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 pa- per, we present a two-stage uniﬁed M ult I -task G eneration fr A mework (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 lan- guage prompts to boost the main task from multi-task training. Later in the ﬁne-tuning stage, we propose four SQL per- turbations to alleviate the error propagation problem. MIGA tends to achieve state-of-the-art performance on two bench- marks (SparC and CoSQL). We also provide extensive analyses and discussions to shed light on some new perspectives for conversational text-to-SQL.