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.

翻译：在ACM KDF-SIGIR 2023竞赛的背景下，我们针对一个名为REFind的金融实体关系数据集开展了实体关系任务。我们最优的解决方案采用了一种多步骤方法。首先，我们将提供的实体插入到文本中对应位置。随后，我们微调基于Transformer的语言模型roberta-large，利用标注训练集进行文本分类，以预测实体关系。最后，我们实施了一个后处理阶段，用于识别并处理模型生成的不可靠预测。通过上述方法，我们在竞赛公开排行榜上取得了第一名。