.png){kind=icon}
返回
M-SQL: Multi-Task Representation Learning for Single-Table Text2sql Generation
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.2977613.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Task analysis
Databases
Bit error rate
Semantics
Syntactics
Decoding
Natural languages
Text2SQL
M-SQL
pre-trained
multi-task learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
分享收藏分享收藏Determining the three-dimensional geometry of a dike swarm and its impact on later rift geometry using seismic reflection data.png)
GeologyOAAI
GeologyIF0
OAAI分享收藏Response of natural smectite to seismogenic heating and potential implications for the 2011 Tohoku earthquake in the Japan TrenchGeology
GeologyIF0
PREAI
分享收藏分享收藏 没有更多内容
没有更多内容