PINE: Extracting Correlated Token Pairs for Explainable Entity Matching

Abstract

Explanation techniques such as local interpretable model-agnostic explanation (LIME) provide reasons behind decisions made by machine-learning models. These methods typically use a set of features and their values as inputs and identify those that significantly influence the final decision. However, machine-learning models for entity matching operate on two sets of tokens or records, each representing an entity, to determine whether they refer to the same real-world entity. Explanations for entity-matching decisions are more convincing when they highlight contributing pairs of tokens within the pair of records, rather than focusing on individual tokens alone. In this sense, existing explanation techniques are insufficient for entity matching. Therefore, we propose a new method, Pair INterpretation for Entity matching (PINE), which takes two records as input, and outputs correlated token pairs as an explanation for an entity-matching decision. Our extensive experiments on public datasets demonstrate that the extracted token pairs exhibit strong correlations and serve as interpretable evidence for matching records.