  In this paper, we introduce a strategy for identifying textual saliency in
large-scale language models applied to classification tasks. In visual networks
where saliency is more well-studied, saliency is naturally localized through
the convolutional layers of the network; however, the same is not true in
modern transformer-stack networks used to process natural language. We adapt
gradient-based saliency methods for these networks, propose a method for
evaluating the degree of semantic coherence of each layer, and demonstrate
consistent improvement over numerous other methods for textual saliency on
multiple benchmark classification datasets. Our approach requires no additional
training or access to labelled data, and is comparatively very computationally
efficient.
