Empowering Transformer with Hybrid Matching Knowledge for Entity Matching

Wenzhou Dou, Derong Shen, Tiezheng Nie, Yue Kou, Chenchen Sun, Hang Cui, Ge Yu

Abstract: Transformers have achieved great success in many NLP tasks. The self-attention mechanism of Transformer learns powerful representation by conducting token-level pairwise interactions within the input sequence. In this paper, we propose a novel entity matching framework named GTA. GTA enhances Transformer for relational data representation by injecting additional hybrid matching knowledge. The hybrid matching knowledge is obtained via graph contrastive learning on a designed hybrid matching graph, in which the dual-level matching and multiple granularity interactions are modeled. In this way, GTA utilizes the prelearned knowledge of both hybrid matching and language modeling. This effectively empowers Transformer to understand the structural features of relational data when performing entity matching. Extensive experiments on open datasets show that GTA effectively enhances Transformer for relational data representation and outperforms state-of-the-art entity matching frameworks.