Document-level Relation Extraction (RE) is a more challenging task than sentence RE as it often requires reasoning over multiple sentences. Yet, human annotators usually use a small number of sentences to identify the relationship between a given entity pair. In this paper, we present an embarrassingly simple but effective method to heuristically select evidence sentences for document-level RE, which can be easily combined with BiLSTM to achieve good performance on benchmark datasets, even better than fancy graph neural network based methods. We have released our code at https://github.com/AndrewZhe/Three-Sentences-Are-All-You-Need.
翻译:文件级关系提取(RE)任务比判刑级Relation Expliton(RE)任务更具挑战性,因为它常常要求对多项判决进行推理。然而,人类告发员通常使用少量的句子来确定特定实体对子之间的关系。在本文中,我们提出了一个令人尴尬的简单而有效的方法,用于为文件级RE(RE)选择证据判决,这很容易与BLSTM结合起来,以便在基准数据集上取得良好的业绩,甚至比基于花哨的图形神经网络方法更好。我们已经在https://github.com/AndrewZhe/ Three-Sentences-Are-All-You-Need公布了我们的代码。