cueEM: human-like entity matching via text-centric hybrid attention

Psychological AI advocates designing algorithms inspired by human cognition. We apply this paradigm to Entity Matching (EM), where high-stakes scenarios demand not only high accuracy but also human-like reasoning to ensure transparency, reliability, and user confidence. However, current deep learning (DL) EM models often act as black boxes, failing to capture human decision-making processes, such as prioritizing diagnostic attributes, inferring missing information from context, and assessing overall semantic consistency. We propose cueEM, a human-like EM model grounded in Cue Validity Theory. By adopting a text-centric design and leveraging Bert-Pair-Networks with a hybrid attention mechanism, cueEM captures and integrates textual features across attributes. It generates attribute-specific weights that explicitly reveal each attribute’s contribution, aligning the model’s decisions with human decision processes. Extensive evaluations on DeepMatcher benchmarks demonstrate that cueEM outperforms Effective Entity Matching with Transformers (DITTO) and achieves competitive performance with large language models (LLMs), while providing inherently human-like reasoning. To our knowledge, cueEM is the first text-focused framework that combines hybrid attention with human-aligned reasoning, enabling interpretable and human-like decision-making.