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

Understand

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.