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.
翻译:从我们的观点来看,文件一级RE必须利用多语种推断信息:实体一级、判刑级别和文件级别。因此,如何以不同颗粒性获得和汇总推断信息对于文件一级RE来说具有挑战性,而以前的工作尚未考虑到这一点。我们在此文件中提议建立一个等级推断网络,以便充分利用实体一级、判刑级别和文件级别上的大量信息。翻译限制和双线转换适用于多个子空间中的目标对口实体,以获得实体一级推断信息。接下来,我们将实体一级信息和句号代表之间的推论建模,以达到判决一级推断信息。最后,采用了等级汇总方法,以获得文件一级推断信息。这样,我们的模型可以有效地汇总这三种不同的颗粒级、判决级别和文件级别上的大量信息。实验结果显示,我们的方法在大规模文件数据库中实现了最新业绩。我们还可以通过大规模文件数据库显示,我们还可以进一步提高业绩。