论文标题
两个比一个更好：联合实体和与表格编码器的关系提取
Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders
论文作者
论文摘要
指定的实体识别和关系提取是两个重要的基本问题。已经提出了联合学习算法来同时解决这两个任务，其中许多任务将联合任务视为解决问题的问题。但是，他们通常专注于学习单个编码器（通常以表格的形式学习表示），以捕获同一空间内两个任务所需的信息。我们认为，设计两个不同的编码器来捕获学习过程中的两种不同类型的信息可能是有益的。在这项工作中，我们提出了小说{\ em Table-sequence编码}，其中两个不同的编码器 - 表编码器和序列编码器旨在在表示过程中相互帮助。我们的实验证实了在{\ em One} encoder上具有{\ em Two}编码器的优势。在几个标准数据集上，我们的模型对现有方法显示出显着改善。
Named entity recognition and relation extraction are two important fundamental problems. Joint learning algorithms have been proposed to solve both tasks simultaneously, and many of them cast the joint task as a table-filling problem. However, they typically focused on learning a single encoder (usually learning representation in the form of a table) to capture information required for both tasks within the same space. We argue that it can be beneficial to design two distinct encoders to capture such two different types of information in the learning process. In this work, we propose the novel {\em table-sequence encoders} where two different encoders -- a table encoder and a sequence encoder are designed to help each other in the representation learning process. Our experiments confirm the advantages of having {\em two} encoders over {\em one} encoder. On several standard datasets, our model shows significant improvements over existing approaches.
