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Causality detection draws plenty of attention in the field of NaturalLanguage Processing and linguistics research. It has essential applications ininformation retrieval, event prediction, question answering, financialanalysis, and market research. In this study, we explore several methods toidentify and extract cause-effect pairs in financial documents usingtransformers. For this purpose, we propose an approach that combines POStagging with the BIO scheme, which can be integrated with modern transformermodels to address this challenge of identifying causality in a given text. Ourbest methodology achieves an F1-Score of 0.9551, and an Exact Match Score of0.8777 on the blind test in the FinCausal-2021 Shared Task at the FinCausal2021 Workshop.

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