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Open-domain conversation models have become good at generatingnatural-sounding dialogue, using very large architectures with billions oftrainable parameters. The vast training data required to train thesearchitectures aggregates many different styles, tones, and qualities. Usingthat data to train a single model makes it difficult to use the model as aconsistent conversational agent, e.g. with a stable set of persona traits and atypical style of expression. Several architectures affording control mechanismsover generation architectures have been proposed, each with differenttrade-offs. However, it remains unclear whether their use in dialogue isviable, and what the trade-offs look like with the most recent state-of-the-artconversational architectures. In this work, we adapt three previously proposedcontrollable generation architectures to open-domain dialogue generation,controlling the style of the generation to match one among about 200 possiblestyles. We compare their respective performance and tradeoffs, and show howthey can be used to provide insights into existing conversational datasets, andgenerate a varied set of styled conversation replies.

Controlling Style in Generated Dialogue