Anaphor Assisted Document-Level Relation Extraction
This work improves relation extraction for entities spread across sentences in documents, though it appears incremental as it builds on existing graph-based methods.
The paper tackles the problem of document-level relation extraction by addressing the neglect of anaphors and the difficulty in learning fine-grained cross-sentence entity interactions, resulting in a new state-of-the-art performance on widely-used datasets.
Document-level relation extraction (DocRE) involves identifying relations between entities distributed in multiple sentences within a document. Existing methods focus on building a heterogeneous document graph to model the internal structure of an entity and the external interaction between entities. However, there are two drawbacks in existing methods. On one hand, anaphor plays an important role in reasoning to identify relations between entities but is ignored by these methods. On the other hand, these methods achieve cross-sentence entity interactions implicitly by utilizing a document or sentences as intermediate nodes. Such an approach has difficulties in learning fine-grained interactions between entities across different sentences, resulting in sub-optimal performance. To address these issues, we propose an Anaphor-Assisted (AA) framework for DocRE tasks. Experimental results on the widely-used datasets demonstrate that our model achieves a new state-of-the-art performance.