在自然语言理解和语义表征的研究中，往往需要验证一句文本陈述是否基于给定的事实证据，这就是事实检测任务.现有的研究主要局限于处理文本事实验证，而结构化证据下的验证还有待探索，比如基于表格等形式的事实验证.TabFact作为最新的基于表格的事实验证数据集，基线方法并没有很好地利用表格的结构性特征.结合表格的结构特征，设计了以行、单元格为单位的基于图神经网络的事实验证模型Row-GVM和Cell-GVM，比基线模型的准确率分别提高了2.62%和2.77%.实验结果表明，这两种利用了表格特征的方法确实是有效的.

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.

国家重点研发计划（2018AAA0101900，2018AAA0101902）；国家自然科学基金（91646202，61772039）