Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders
This work addresses the problem of improving joint entity and relation extraction for natural language processing applications, representing an incremental advancement by introducing a dual-encoder design.
The paper tackled the joint task of named entity recognition and relation extraction by proposing a novel table-sequence encoder architecture with two distinct encoders, resulting in significant improvements over existing approaches on standard datasets.
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.