  Relational triple extraction is challenging for its difficulty in capturing
rich correlations between entities and relations. Existing works suffer from 1)
heterogeneous representations of entities and relations, and 2) heterogeneous
modeling of entity-entity interactions and entity-relation interactions.
Therefore, the rich correlations are not fully exploited by existing works. In
this paper, we propose UniRel to address these challenges. Specifically, we
unify the representations of entities and relations by jointly encoding them
within a concatenated natural language sequence, and unify the modeling of
interactions with a proposed Interaction Map, which is built upon the
off-the-shelf self-attention mechanism within any Transformer block. With
comprehensive experiments on two popular relational triple extraction datasets,
we demonstrate that UniRel is more effective and computationally efficient. The
source code is available at https://github.com/wtangdev/UniRel.
