  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.
