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Home > Archive>Volume 32, Issue 3, 2021 >753-762. DOI:10.13328/j.cnki.jos.006184
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Graph Neural Networks for Table-based Fact Verification
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Key Research and Development Program of China (2018AAA0101900, 2018AAA0101902); National Natural Science Foundation of China (91646202, 61772039)

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    Abstract:

    In the study of natural language understanding and semantic representation, the fact verification task is very important to verify whether a textual statement is based on given factual evidence. Existing research is mainly limited to dealing with textual fact verification, while verification under structured evidence has yet to be explored, such as fact verification based on forms. TabFact is the latest table-based fact verification data set, but the baseline methods do not make good use of the structural characteristics of the table. This study takes advantage of the structural characteristics of the table and designs two models, Row-GVM (Row-level GNN-based verification model) and Cell-GVM (cell-level GNN-based verification model). They have achieved performances of 2.62% and 2.77% higher than the baseline model respectively. The results prove that these two methods using table features are indeed effective.

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邓哲也,张铭.用于表格事实检测的图神经网络模型.软件学报,2021,32(3):753-762

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History
  • Received:August 23,2020
  • Revised:September 03,2020
  • Online: January 21,2021
  • Published: March 06,2021
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