A Multiscale Visualization of Attention in the Transformer Model

The Transformer is a sequence model that forgoes traditional recurrent architectures in favor of a fully attention-based approach.Besides improving performance, an advantage of using attention is that it can also help to interpret a model by showing how the model assigns weight to different input elements.However, the multi-layer, multi-head attention mechanism in the Transformer model can be difficult to decipher.To make the model more accessible, we introduce an open-source tool that visualizes attention at multiple scales, each of which provides a unique perspective on the attention mechanism.We demonstrate the tool on BERT and OpenAI GPT-2 and present three example use cases: detecting model bias, locating relevant attention heads, and linking neurons to model behavior.