Tables are widely used in various kinds of documents to present information concisely. Understanding tables is a challenging problem that requires an understanding of language and table structure, along with numerical and logical reasoning. In this paper, we present our systems to solve Task 9 of SemEval-2021: Statement Verification and Evidence Finding with Tables (SEM-TAB-FACTS). The task consists of two subtasks: (A) Given a table and a statement, predicting whether the table supports the statement and (B) Predicting which cells in the table provide evidence for/against the statement. We fine-tune TAPAS (a model which extends BERT's architecture to capture tabular structure) for both the subtasks as it has shown state-of-the-art performance in various table understanding tasks. In subtask A, we evaluate how transfer learning and standardizing tables to have a single header row improves TAPAS' performance. In subtask B, we evaluate how different fine-tuning strategies can improve TAPAS' performance. Our systems achieve an F1 score of 67.34 in subtask A three-way classification, 72.89 in subtask A two-way classification, and 62.95 in subtask B.
翻译:理解表格是一个具有挑战性的问题,需要理解语言和表格结构,同时提供数字和逻辑推理。在本文中,我们介绍了解决SemEval-2021任务9的系统:与表格(SEM-TAB-FACTS)一起的报表核查和证据调查。任务由两个子任务组成:(A) 鉴于一个表格和一个说明,预测表格是否支持该表,预测表格中哪个单元格为报表提供证据/对报表提供证据;(B) 预测表中哪些单元格为报表提供证据/对报表提供证据。我们对两个子任务都进行了微调TASAPAS(一种将BERET的架构扩展为表结构的模型,以捕捉表格结构),对两个子任务都进行了微调TASAPS(S-TA)A(S-TA)A(S)AF1分为67.34分,对A3级、A级为72.89k。