SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction
Shuang Zeng, Yuting Wu, Baobao Chang · 2021
Document-level relation extraction has attracted much attention in recent years.It is usually formulated as a classification problem that predicts relations for all entity pairs in the document.However, previous works indiscriminately represent intra-and inter-sentential relations in the same way, confounding the different patterns for predicting them.Besides, they create a document graph and use paths between entities on the graph as clues for logical reasoning.However, not all entity pairs can be connected with a path and have the correct logical reasoning paths in their graph.Thus many cases of logical reasoning cannot be covered.This paper proposes an effective architecture, SIRE, to represent intra-and inter-sentential relations in different ways.We design a new and straightforward form of logical reasoning module that can cover more logical reasoning chains.Experiments on the public datasets show SIRE outperforms the previous state-of-the-art methods.Further analysis shows that our predictions are reliable and explainable.