AttViz: Online exploration of self-attention for transparent neural language modeling
This work addresses the interpretability challenge for researchers and practitioners using large neural language models, though it is incremental as it builds on existing attention mechanisms with a new visualization tool.
The authors tackled the problem of interpretability in neural language models by developing AttViz, an online toolkit for visualizing self-attention mechanisms, enabling users to explore and understand model decisions through interactive visualizations of attention heads and their aggregations.
Neural language models are becoming the prevailing methodology for the tasks of query answering, text classification, disambiguation, completion and translation. Commonly comprised of hundreds of millions of parameters, these neural network models offer state-of-the-art performance at the cost of interpretability; humans are no longer capable of tracing and understanding how decisions are being made. The attention mechanism, introduced initially for the task of translation, has been successfully adopted for other language-related tasks. We propose AttViz, an online toolkit for exploration of self-attention---real values associated with individual text tokens. We show how existing deep learning pipelines can produce outputs suitable for AttViz, offering novel visualizations of the attention heads and their aggregations with minimal effort, online. We show on examples of news segments how the proposed system can be used to inspect and potentially better understand what a model has learned (or emphasized).