  In this paper we introduce a method for visually analyzing contextualized
embeddings produced by deep neural network-based language models. Our approach
is inspired by linguistic probes for natural language processing, where tasks
are designed to probe language models for linguistic structure, such as
parts-of-speech and named entities. These approaches are largely confirmatory,
however, only enabling a user to test for information known a priori. In this
work, we eschew supervised probing tasks, and advocate for unsupervised probes,
coupled with visual exploration techniques, to assess what is learned by
language models. Specifically, we cluster contextualized embeddings produced
from a large text corpus, and introduce a visualization design based on this
clustering and textual structure - cluster co-occurrences, cluster spans, and
cluster-word membership - to help elicit the functionality of, and relationship
between, individual clusters. User feedback highlights the benefits of our
design in discovering different types of linguistic structures.
