  The introduction of Transformer neural networks has changed the landscape of
Natural Language Processing (NLP) during the last years. So far, none of the
visualization systems has yet managed to examine all the facets of the
Transformers. This gave us the motivation of the current work. We propose a new
NLP Transformer context-sensitive visualization method that leverages existing
NLP tools to find the most significant groups of tokens (words) that have the
greatest effect on the output, thus preserving some context from the original
text. First, we use a sentence-level dependency parser to highlight promising
word groups. The dependency parser creates a tree of relationships between the
words in the sentence. Next, we systematically remove adjacent and non-adjacent
tuples of \emph{n} tokens from the input text, producing several new texts with
those tokens missing. The resulting texts are then passed to a pre-trained BERT
model. The classification output is compared with that of the full text, and
the difference in the activation strength is recorded. The modified texts that
produce the largest difference in the target classification output neuron are
selected, and the combination of removed words are then considered to be the
most influential on the model's output. Finally, the most influential word
combinations are visualized in a heatmap.
