  Transformer networks have revolutionized NLP representation learning since
they were introduced. Though a great effort has been made to explain the
representation in transformers, it is widely recognized that our understanding
is not sufficient. One important reason is that there lack enough visualization
tools for detailed analysis. In this paper, we propose to use dictionary
learning to open up these `black boxes' as linear superpositions of transformer
factors. Through visualization, we demonstrate the hierarchical semantic
structures captured by the transformer factors, e.g. word-level polysemy
disambiguation, sentence-level pattern formation, and long-range dependency.
While some of these patterns confirm the conventional prior linguistic
knowledge, the rest are relatively unexpected, which may provide new insights.
We hope this visualization tool can bring further knowledge and a better
understanding of how transformer networks work.
