SPOCK at FinCausal 2022: Causal Information Extraction Using Span-Based and Sequence Tagging Models

Abstract

Causal information extraction is an important task in natural language processing, particularly in finance domain. In this work, we develop several information extraction models using pre-trained transformer-based language models for identifying cause and effect text spans from financial documents. We use FinCausal 2021 and 2022 data sets to train span-based and sequence tagging models. Our ensemble of sequence tagging models based on the RoBERTa-Large pre-trained language model achieves an F1 score of 94.70 with Exact Match score of 85.85 and obtains the 1st place in the FinCausal 2022 competition.