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.

本文介绍了一种新的表序列编码器，用于联合学习命名实体识别和关系提取这两个重要的基础问题，由于这种编码器由两个不同的编码器组成，可以在学习过程中捕捉到两种不同类型的信息，相较于现有方法，此模型在多个标准数据集上表现出了显著的改进。