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Named entity recognition and relation extraction are two importantfundamental problems. Joint learning algorithms have been proposed to solveboth tasks simultaneously, and many of them cast the joint task as atable-filling problem. However, they typically focused on learning a singleencoder (usually learning representation in the form of a table) to captureinformation required for both tasks within the same space. We argue that it canbe beneficial to design two distinct encoders to capture such two differenttypes of information in the learning process. In this work, we propose thenovel {\em table-sequence encoders} where two different encoders -- a tableencoder and a sequence encoder are designed to help each other in therepresentation learning process. Our experiments confirm the advantages ofhaving {\em two} encoders over {\em one} encoder. On several standard datasets,our model shows significant improvements over existing approaches.

Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders