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

UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction