  During the last decade, Natural Language Processing has become, after
Computer Vision, the second field of Artificial Intelligence that was massively
changed by the advent of Deep Learning. Regardless of the architecture, the
language models of the day need to be able to process or generate text, as well
as predict missing words, sentences or relations depending on the task. Due to
their black-box nature, such models are difficult to interpret and explain to
third parties. Visualization is often the bridge that language model designers
use to explain their work, as the coloring of the salient words and phrases,
clustering or neuron activations can be used to quickly understand the
underlying models. This paper showcases the techniques used in some of the most
popular Deep Learning for NLP visualizations, with a special focus on
interpretability and explainability.
