Interpreting BERT-based Text Similarity via Activation and Saliency Maps

Abstract

Recently, there has been growing interest in the ability of Transformerbased 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.

Global reconstruction of language models with linguistic rules – Explainable AI for online consumer reviews

“…Ad category C): Moreover, local non-rule-based XAI approaches have been proposed to reason language model predictions. In Malkiel et al (2022), saliency maps are used to reason similarity predictions of online consumer reviews by a BERT-based model, aiming to highlight important word-pairs for specific similarity predictions. Moreover, different visualizations with respect to neuron activations in the hidden layers have been applied to reason specific language model predictions (Brasoveanu & Andonie, 2022).…”