Visualizing and Measuring the Geometry of BERT

Part of Advances in Neural Information Processing Systems 32 (NeurIPS 2019)

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Emily Reif, Ann Yuan, Martin Wattenberg, Fernanda B. Viegas, Andy Coenen, Adam Pearce, Been Kim


<p>Transformer architectures show significant promise for natural language processing. Given that a single pretrained model can be fine-tuned to perform well on many different tasks, these networks appear to extract generally useful linguistic features. A natural question is how such networks represent this information internally. This paper describes qualitative and quantitative investigations of one particularly effective model, BERT. At a high level, linguistic features seem to be represented in separate semantic and syntactic subspaces. We find evidence of a fine-grained geometric representation of word senses. We also present empirical descriptions of syntactic representations in both attention matrices and individual word embeddings, as well as a mathematical argument to explain the geometry of these representations.</p>