Volta at SemEval-2021 Task 9: Statement Verification and Evidence Finding with Tables using TAPAS and Transfer Learning

TL;DR

This paper presents a system using TAPAS and transfer learning to improve statement verification and evidence finding in tables, achieving competitive F1 scores in SemEval-2021 Task 9.

Contribution

It introduces fine-tuning strategies for TAPAS on table understanding tasks, enhancing performance in statement support prediction and evidence identification.

Findings

Transfer learning improves TAPAS performance.

Standardizing table headers benefits classification accuracy.

Fine-tuning strategies enhance evidence cell prediction.

Abstract

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.