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M-SQL: Multi-Task Representation Learning for Single-Table Text2sql Generation
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.
论文下载
论文地址:https://ieeexplore.ieee.org/document/9020099
算法链接
算法:https://marketplace.huaweicloud.com/markets/aihub/modelhub/detail/?id=4d1c0887-8ef0-4133-bd02-c4d69255377a
算法指南
算法指南:https://bbs.huaweicloud.com/forum/thread-101022-1-1.html
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这一瞬间有一百万个可能~
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