  This paper investigates contextual word representation models from the lens
of similarity analysis. Given a collection of trained models, we measure the
similarity of their internal representations and attention. Critically, these
models come from vastly different architectures. We use existing and novel
similarity measures that aim to gauge the level of localization of information
in the deep models, and facilitate the investigation of which design factors
affect model similarity, without requiring any external linguistic annotation.
The analysis reveals that models within the same family are more similar to one
another, as may be expected. Surprisingly, different architectures have rather
similar representations, but different individual neurons. We also observed
differences in information localization in lower and higher layers and found
that higher layers are more affected by fine-tuning on downstream tasks.
