论文标题
具有潜在结构改进的推理，用于文档级别的关系提取
Reasoning with Latent Structure Refinement for Document-Level Relation Extraction
论文作者
论文摘要
文档级别的关系提取需要在文档的多个句子内和跨越句子间实体之间的复杂相互作用。但是，文档中相关信息的有效汇总仍然是一个具有挑战性的研究问题。现有方法基于句法树，共同参考或非结构化文本的启发式构建静态文档级图，以建模依赖关系。与以前可能无法捕获有关推理的丰富非本地相互作用的方法不同，我们提出了一个新型模型，该模型通过自动诱导潜在文档级别的图来赋予跨句子的关系推理。我们进一步制定了一种改进策略，该策略使该模型能够逐步汇总相关信息以进行多跳上的推理。具体而言，我们的模型在大规模文档级数据集（DOCRED）上达到了59.05的F1分数，对先前结果显着改善，并且在CDR和GDA数据集上也产生了新的最新结果。此外，广泛的分析表明，该模型能够发现更准确的句子间关系。
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 F1 score of 59.05 on a large-scale document-level 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.
