Sentence-level relation extraction (RE) aims to identify the relationship between 2 entities given a contextual sentence. While there have been many attempts to solve this problem, the current solutions have a lot of room to improve. In this paper, we approach the task of relationship extraction in the financial dataset REFinD. Our approach incorporates typed entity markers representations and various models finetuned on the dataset, which has allowed us to achieve an F1 score of 69.65% on the validation set. Through this paper, we discuss various approaches and possible limitations.

翻译：句子级关系抽取（RE）旨在根据上下文句子识别两个实体之间的关系。尽管已有众多研究尝试解决该问题，但现有方案仍有较大改进空间。本文针对金融数据集REFinD中的关系抽取任务展开研究，通过融合带类型的实体标记表示，并结合在该数据集上微调的多类模型，在验证集上取得了69.65%的F1分数。在本文中，我们探讨了多种实现方案及其潜在局限性。