IIT_kgp at FinCausal 2020, Shared Task 1: Causality Detection using Sentence Embeddings in Financial Reports

TL;DR

This paper presents a system for detecting causality in financial report sentences using sentence embeddings, with BERT achieving high accuracy in the FinCausal 2020 shared task.

Contribution

The work introduces a causality detection approach leveraging sentence embeddings and a modified loss function, achieving state-of-the-art results in financial text analysis.

Findings

BERT (Large) achieved an F1 score of 0.958.

Modified loss function improved class imbalance handling.

Sentence embeddings effectively represent causality in financial texts.

Abstract

The paper describes the work that the team submitted to FinCausal 2020 Shared Task. This work is associated with the first sub-task of identifying causality in sentences. The various models used in the experiments tried to obtain a latent space representation for each of the sentences. Linear regression was performed on these representations to classify whether the sentence is causal or not. The experiments have shown BERT (Large) performed the best, giving a F1 score of 0.958, in the task of detecting the causality of sentences in financial texts and reports. The class imbalance was dealt with a modified loss function to give a better metric score for the evaluation.