BreakingBERT@IITK at SemEval-2021 Task 9 : Statement Verification and Evidence Finding with Tables

TL;DR

This paper presents CellBERT, a novel model for fact verification and evidence finding in tables, achieving competitive results on the SemTabFact dataset by treating evidence finding as a Natural Language Inference task.

Contribution

The paper introduces CellBERT, a new approach for evidence finding in tabular data, and provides a comprehensive comparison with existing methods on SemTabFact.

Findings

CellBERT achieves 0.69 F1 on statement verification.

CellBERT achieves 0.65 F1 on evidence finding.

The approach outperforms baseline methods.

Abstract

Recently, there has been an interest in factual verification and prediction over structured data like tables and graphs. To circumvent any false news incident, it is necessary to not only model and predict over structured data efficiently but also to explain those predictions. In this paper, as part of the SemEval-2021 Task 9, we tackle the problem of fact verification and evidence finding over tabular data. There are two subtasks. Given a table and a statement/fact, subtask A determines whether the statement is inferred from the tabular data, and subtask B determines which cells in the table provide evidence for the former subtask. We make a comparison of the baselines and state-of-the-art approaches over the given SemTabFact dataset. We also propose a novel approach CellBERT to solve evidence finding as a form of the Natural Language Inference task. We obtain a 3-way F1 score of 0.69 on subtask A and an F1 score of 0.65 on subtask B.