M-SQL: Multi-Task Representation Learning for Single-Table Text2sql Generation

Xiaoyu Zhang

doi:10.1109/ACCESS.2020.2977613

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M-SQL: Multi-Task Representation Learning for Single-Table Text2sql Generation

2020-01-01

DOI：10.1109/ACCESS.2020.2977613

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Abstract

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Text2SQL can help non-professionals connect with databases by turning natural languages into SQL. Although previous researches about Text2SQL have provided some workable solutions, most of them extract values based on column representation. If there are multiple values in the query and these values belong to different columns, the previous approaches based on column representation cannot accurately extract values. In this work, we propose a new neural network architecture based on the pre-trained BERT, called M-SQL. The column-based value extraction is divided into two modules, value extraction and value-column matching. We evaluate M-SQL on a more complicated TableQA dataset, which comes from an AI competition. We rank first in this competition. Experimental results and competition ranking show that our proposed M-SQL achieves state-of-the-art results on TableQA.

Keywords:

Task analysis

Databases

Bit error rate

Semantics

Syntactics

Decoding

Natural languages

Text2SQL

M-SQL

pre-trained

multi-task learning

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