  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.
