  Recent works on relational triple extraction have shown the superiority of
jointly extracting entities and relations over the pipelined extraction manner.
However, most existing joint models fail to balance the modeling of entity
features and the joint decoding strategy, and thus the interactions between the
entity level and triple level are not fully investigated. In this work, we
first introduce the hierarchical dependency and horizontal commonality between
the two levels, and then propose an entity-enhanced dual tagging framework that
enables the triple extraction (TE) task to utilize such interactions with
self-learned entity features through an auxiliary entity extraction (EE) task,
without breaking the joint decoding of relational triples. Specifically, we
align the EE and TE tasks in a position-wise manner by formulating them as two
sequence labeling problems with identical encoder-decoder structure. Moreover,
the two tasks are organized in a carefully designed parameter sharing setting
so that the learned entity features could be naturally shared via multi-task
learning. Empirical experiments on the NYT benchmark demonstrate the
effectiveness of the proposed framework compared to the state-of-the-art
methods.
