TSCL-SQL:Two-Stage Curriculum Learning Framework for Text-to-SQL

• Published in: 东华大学学报（英文版） Vol. 40; no. 4; pp. 421 - 427
• Main Authors: YIN Feng, CHENG Luyi, WANG Qiuyue, WANG Zhijun, DU Ming, XU Bo
• Format: Journal Article
• Language: English
• Published: School of Computer Science and Technology,Donghua University,Shanghai 201620,China 31.08.2023
• Subjects: semantic parsing; curriculum learning; text-to-SQL
• ISSN: 1672-5220
• DOI: 10.19884/j.1672-5220.202207003

TP391.1; Text-to-SQL is the task of translating a natural language query into a structured query language.Existing text-to-SQL approaches focus on improving the model's architecture while ignoring the relationship between queries and table schemas and the differences in difficulty between examples in the dataset.To tackle these challenges,a two-stage curriculum learning framework for text-to-SQL(TSCL-SQL)is proposed in this paper.To exploit the relationship between the queries and the table schemas,a schema identification pre-training task is proposed to make the model choose the correct table schema from a set of candidates for a specific query.To leverage the differences in difficulty between examples,curriculum learning is applied to the text-to-SQL task,accompanied by an automatic curriculum learning solution,including a difficulty scorer and a training scheduler.Experiments show that the framework proposed in this paper is effective.