Explaining Entity Matching with Clusters of Words

Deep learning models achieve state-of-the-art per-formance in solving the task of Entity Matching, which aims to identify records that refer to the same real-world entity. However, they act as black-box models for the user, who has limited insights into the rationales behind their decisions. Several explainers (e.g., LIME, Mojito, Landmark, LEMON, and CERTA) have been proposed in the literature to address this issue. Their main focus is to generate explanations that are faithful to the model without considering their comprehensibility to the user. For example, verbose explanations could be very complex to analyze, hindering the model's understanding. In this paper, we propose CREW, an explanation system for Entity Matching models that combines the comprehensibility of the explanations and fidelity to the model. To achieve this, CREW creates explanations as clusters of words. The clusters are created by exploiting three different forms of knowledge: the semantic similarity of the words, their arrangement into the dataset attributes, and their importance in explaining the model. Experiments show that CREW generates explanations that are more interpretable for the user and more faithful to the model than those generated by competing explanation techniques.