  We investigate how different domains are encoded in modern neural network
architectures. We analyze the relationship between natural language domains,
model size, and the amount of training data used. The primary analysis tool we
develop is based on subpopulation analysis with Singular Vector Canonical
Correlation Analysis (SVCCA), which we apply to Transformer-based language
models (LMs). We compare the latent representations of such a language model at
its different layers from a pair of models: a model trained on multiple domains
(an experimental model) and a model trained on a single domain (a control
model). Through our method, we find that increasing the model capacity impacts
how domain information is stored in upper and lower layers differently. In
addition, we show that larger experimental models simultaneously embed
domain-specific information as if they were conjoined control models. These
findings are confirmed qualitatively, demonstrating the validity of our method.
