Evidence Sentence Augmented Sequence-to-Sequence Method for Document-level Relation Extraction

Document-level relation extraction is an important task in natural language processing that involves identifying and classifying relations between entities mentioned in a document. Traditional approaches often focus on individual sentences or local context, overlooking the broader context of the entire document. In this paper, we propose an Evidence Sentence Augmented Sequence-to-Sequence method for Document-level Relation Extraction(called ESASS-DRE). Our method introduces evidence sentences into the sequence-to-sequence framework to improve document-level relation extraction. The approach consists of two main steps: evidence sentence selection and relation extraction. Firstly, we identify a set of evidence sentences that contain crucial information relevant to the target relation. These sentences are selected based on their importance and contextual relevance. Secondly, the selected evidence sentences are combined with the original document and used as input to the sequence-to-sequence model. These generated sequences are decoded into relation labels, indicating the type of relationship between the entities. By incorporating evidence sentences into the model, we provide additional context and relevant information, enabling the model to make more informed predictions. Experiments conducted on benchmark datasets demonstrate the effectiveness of our method. Compared to traditional approaches, our method achieves higher accuracy and robustness in document-level relation extraction tasks. The incorporation of evidence sentences allows the model to capture the broader context of the document, leading to improved performance. (e.g., by 2.96/3.64 Ign F1/F1 on DocRED).