DSC-IITISM at FinCausal 2021: Combining POS tagging with Attention-based Contextual Representations for Identifying Causal Relationships in Financial Documents

TL;DR

This paper presents a method combining POS tagging with transformer models to effectively identify causal relationships in financial documents, achieving high accuracy in a shared task setting.

Contribution

It introduces a novel approach that integrates POS tagging with transformer-based models for causality detection in financial texts, improving performance over existing methods.

Findings

Achieved an F1-Score of 0.9551 on the test set.

Attained an Exact Match Score of 0.8777.

Demonstrated effectiveness in the FinCausal 2021 shared task.

Abstract

Causality detection draws plenty of attention in the field of Natural Language Processing and linguistics research. It has essential applications in information retrieval, event prediction, question answering, financial analysis, and market research. In this study, we explore several methods to identify and extract cause-effect pairs in financial documents using transformers. For this purpose, we propose an approach that combines POS tagging with the BIO scheme, which can be integrated with modern transformer models to address this challenge of identifying causality in a given text. Our best methodology achieves an F1-Score of 0.9551, and an Exact Match Score of 0.8777 on the blind test in the FinCausal-2021 Shared Task at the FinCausal 2021 Workshop.