  Explaining neural network models is important for increasing their
trustworthiness in real-world applications. Most existing methods generate
post-hoc explanations for neural network models by identifying individual
feature attributions or detecting interactions between adjacent features.
However, for models with text pairs as inputs (e.g., paraphrase
identification), existing methods are not sufficient to capture feature
interactions between two texts and their simple extension of computing all
word-pair interactions between two texts is computationally inefficient. In
this work, we propose the Group Mask (GMASK) method to implicitly detect word
correlations by grouping correlated words from the input text pair together and
measure their contribution to the corresponding NLP tasks as a whole. The
proposed method is evaluated with two different model architectures
(decomposable attention model and BERT) across four datasets, including natural
language inference and paraphrase identification tasks. Experiments show the
effectiveness of GMASK in providing faithful explanations to these models.
