HIN: Hierarchical Inference Network for Document-Level Relation Extraction
This addresses the challenge of extracting relations across multiple sentences in documents, which is incremental as it builds on prior work by explicitly modeling multi-granularity inference.
The paper tackles document-level relation extraction by proposing a Hierarchical Inference Network (HIN) that aggregates multi-granularity inference information from entity, sentence, and document levels, achieving state-of-the-art performance on the DocRED dataset.
Document-level RE requires reading, inferring and aggregating over multiple sentences. From our point of view, it is necessary for document-level RE to take advantage of multi-granularity inference information: entity level, sentence level and document level. Thus, how to obtain and aggregate the inference information with different granularity is challenging for document-level RE, which has not been considered by previous work. In this paper, we propose a Hierarchical Inference Network (HIN) to make full use of the abundant information from entity level, sentence level and document level. Translation constraint and bilinear transformation are applied to target entity pair in multiple subspaces to get entity-level inference information. Next, we model the inference between entity-level information and sentence representation to achieve sentence-level inference information. Finally, a hierarchical aggregation approach is adopted to obtain the document-level inference information. In this way, our model can effectively aggregate inference information from these three different granularities. Experimental results show that our method achieves state-of-the-art performance on the large-scale DocRED dataset. We also demonstrate that using BERT representations can further substantially boost the performance.