A Survey on Table Question Answering: Recent Advances

被引:10
作者
Jin, Nengzheng [1 ]
Siebert, Joanna [1 ]
Li, Dongfang [1 ]
Chen, Qingcai [1 ,2 ]
机构
[1] Harbin Inst Technol Shenzhen, Shenzhen, Peoples R China
[2] Peng Cheng Lab, Shenzhen, Peoples R China
来源
KNOWLEDGE GRAPH AND SEMANTIC COMPUTING: KNOWLEDGE GRAPH EMPOWERS THE DIGITAL ECONOMY, CCKS 2022 | 2022年 / 1669卷
关键词
Natural language processing; Table QA; Semantic parsing;
D O I
10.1007/978-981-19-7596-7_14
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Table Question Answering (Table QA) refers to providing precise answers from tables to answer a user's question. In recent years, there have been a lot of works on table QA, but there is a lack of comprehensive surveys on this research topic. Hence, we aim to provide an overview of available datasets and representative methods in table QA. We classify existing methods for table QA into five categories according to their techniques, which include semantic-parsing-based, generative, extractive, matching-based, and retriever-reader-based methods. Moreover, because table QA is still a challenging task for existing methods, we also identify and outline several key challenges and discuss the potential future directions of table QA.
引用
收藏
页码:174 / 186
页数:13
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