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Ahead of the Text: Leveraging Entity Preposition for Financial Relation Extraction
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In the context of the ACM KDF-SIGIR 2023 competition, we undertook an entity relation task on a dataset of financial entity relations called REFind. Our top-performing solution involved a multi-step approach. Initially, we inserted the provided entities at their corresponding locations within the text. Subsequently, we fine-tuned the transformer-based language model roberta-large for text classification by utilizing a labeled training set to predict the entity relations. Lastly, we implemented a post-processing phase to identify and handle improbable predictions generated by the model. As a result of our methodology, we achieved the 1st place ranking on the competition's public leaderboard.
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A Pluggable Multi-Task Learning Framework for Sentiment-Aware Financial Relation Extraction
A pluggable auxiliary sentiment and dependency-path supervision module improves F1 for most tested relation extraction models on REFinD and TACRED.
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