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Recently, there has been growing interest in the ability of Transformer-basedmodels to produce meaningful embeddings of text with several applications, suchas text similarity. Despite significant progress in the field, the explanationsfor similarity predictions remain challenging, especially in unsupervisedsettings. In this work, we present an unsupervised technique for explainingparagraph similarities inferred by pre-trained BERT models. By looking at apair of paragraphs, our technique identifies important words that dictate eachparagraph's semantics, matches between the words in both paragraphs, andretrieves the most important pairs that explain the similarity between the two.The method, which has been assessed by extensive human evaluations anddemonstrated on datasets comprising long and complex paragraphs, has showngreat promise, providing accurate interpretations that correlate better withhuman perceptions.

Interpreting BERT-based Text Similarity via Activation and Saliency Maps