论文标题
文档级别的关系提取和句子重要性估计和聚焦
Document-Level Relation Extraction with Sentences Importance Estimation and Focusing
论文作者
论文摘要
文档级别的关系提取（DOCRE）旨在从多个句子的文档中确定两个实体之间的关系。最近的研究通常按序列或基于图的模型表示整个文档，以预测所有实体对的关系。但是，我们发现这样的模型不健壮，并且表现出奇异的行为：它可以正确预测何时将整个测试文档作为输入馈送时，但是当删除非证据句子时会出现错误。为此，我们提出了句子重要性估计和焦点（SIEF）框架的句子框架，在那里我们设计了一个句子的重要性得分和集中句子的句子，鼓励DOCRE模型专注于证据句子。两个领域的实验结果表明，我们的筛分不仅可以提高整体性能，还可以使DOCRE模型更强大。此外，SIEF是一个通用框架，与各种基本DOCRE模型相结合时，证明是有效的。
Document-level relation extraction (DocRE) aims to determine the relation between two entities from a document of multiple sentences. Recent studies typically represent the entire document by sequence- or graph-based models to predict the relations of all entity pairs. However, we find that such a model is not robust and exhibits bizarre behaviors: it predicts correctly when an entire test document is fed as input, but errs when non-evidence sentences are removed. To this end, we propose a Sentence Importance Estimation and Focusing (SIEF) framework for DocRE, where we design a sentence importance score and a sentence focusing loss, encouraging DocRE models to focus on evidence sentences. Experimental results on two domains show that our SIEF not only improves overall performance, but also makes DocRE models more robust. Moreover, SIEF is a general framework, shown to be effective when combined with a variety of base DocRE models.
