A Survey on Table Question Answering: Recent Advances

Nengzheng Jin, Joanna Siebert, Dongfang Li, Qingcai Chen
7/12/2022
cs.CLcs.AI

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Abstract

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, as 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.

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@article{jin2022survey,
  title  = {A Survey on Table Question Answering: Recent Advances},
  author = {Nengzheng Jin and Joanna Siebert and Dongfang Li and Qingcai Chen},
  year   = {2022},
  eprint = {2207.05270},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2207.05270v1}
}

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