Reasoning with Latent Structure Refinement for Document-Level Relation Extraction
Guoshun Nan, Zhijiang Guo, Ivan Sekulić, Wei Lu · 2020
Document-level relation extraction requires integrating information within and across multiple sentences of a document and capturing complex interactions between inter-sentence entities.However, effective aggregation of relevant information in the document remains a challenging research question.Existing approaches construct static document-level graphs based on syntactic trees, co-references or heuristics from the unstructured text to model the dependencies.Unlike previous methods that may not be able to capture rich non-local interactions for inference, we propose a novel model that empowers the relational reasoning across sentences by automatically inducing the latent document-level graph.We further develop a refinement strategy, which enables the model to incrementally aggregate relevant information for multi-hop reasoning.Specifically, our model achieves an F 1 score of 59.05 on a large-scale documentlevel dataset (DocRED), significantly improving over the previous results, and also yields new state-of-the-art results on the CDR and GDA dataset.Furthermore, extensive analyses show that the model is able to discover more accurate inter-sentence relations.