Rank 2 on FinCausal 2021 (Best Exact Match Score worldwide) boosted using Simple Moding Ensembling of Attention Based Models https://www.researchgate.net/publication/355841871_DSC-IITISM_at_FinCausal_2021_Combining_POS_tagging_with_Attention-based_Contextual_Representations_for_Identifying_Causal_Relationships_in_Financial_Documents
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.
Work in progress (on the complete pipeline)..........
P.S.: Credits to all researchers who have previously worked on this and have inspired us to make a few advancements in the same. Thanks to all.