  Why do large pre-trained transformer-based models perform so well across a
wide variety of NLP tasks? Recent research suggests the key may lie in
multi-headed attention mechanism's ability to learn and represent linguistic
information. Understanding how these models represent both syntactic and
semantic knowledge is vital to investigate why they succeed and fail, what they
have learned, and how they can improve. We present Dodrio, an open-source
interactive visualization tool to help NLP researchers and practitioners
analyze attention mechanisms in transformer-based models with linguistic
knowledge. Dodrio tightly integrates an overview that summarizes the roles of
different attention heads, and detailed views that help users compare attention
weights with the syntactic structure and semantic information in the input
text. To facilitate the visual comparison of attention weights and linguistic
knowledge, Dodrio applies different graph visualization techniques to represent
attention weights scalable to longer input text. Case studies highlight how
Dodrio provides insights into understanding the attention mechanism in
transformer-based models. Dodrio is available at
https://poloclub.github.io/dodrio/.
