  Open-domain conversation models have become good at generating
natural-sounding dialogue, using very large architectures with billions of
trainable parameters. The vast training data required to train these
architectures aggregates many different styles, tones, and qualities. Using
that data to train a single model makes it difficult to use the model as a
consistent conversational agent, e.g. with a stable set of persona traits and a
typical style of expression. Several architectures affording control mechanisms
over generation architectures have been proposed, each with different
trade-offs. However, it remains unclear whether their use in dialogue is
viable, and what the trade-offs look like with the most recent state-of-the-art
conversational architectures. In this work, we adapt three previously proposed
controllable generation architectures to open-domain dialogue generation,
controlling the style of the generation to match one among about 200 possible
styles. We compare their respective performance and tradeoffs, and show how
they can be used to provide insights into existing conversational datasets, and
generate a varied set of styled conversation replies.
