  Recently, there has been growing interest in the ability of Transformer-based
models to produce meaningful embeddings of text with several applications, such
as text similarity. Despite significant progress in the field, the explanations
for similarity predictions remain challenging, especially in unsupervised
settings. In this work, we present an unsupervised technique for explaining
paragraph similarities inferred by pre-trained BERT models. By looking at a
pair of paragraphs, our technique identifies important words that dictate each
paragraph's semantics, matches between the words in both paragraphs, and
retrieves the most important pairs that explain the similarity between the two.
The method, which has been assessed by extensive human evaluations and
demonstrated on datasets comprising long and complex paragraphs, has shown
great promise, providing accurate interpretations that correlate better with
human perceptions.
