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Neural language models are becoming the prevailing methodology for the tasksof query answering, text classification, disambiguation, completion andtranslation. Commonly comprised of hundreds of millions of parameters, theseneural network models offer state-of-the-art performance at the cost ofinterpretability; humans are no longer capable of tracing and understanding howdecisions are being made. The attention mechanism, introduced initially for thetask of translation, has been successfully adopted for other language-relatedtasks. We propose AttViz, an online toolkit for exploration ofself-attention---real values associated with individual text tokens. We showhow existing deep learning pipelines can produce outputs suitable for AttViz,offering novel visualizations of the attention heads and their aggregationswith minimal effort, online. We show on examples of news segments how theproposed system can be used to inspect and potentially better understand what amodel has learned (or emphasized).

AttViz: Online exploration of self-attention for transparent neural language modeling