  Non-goal oriented dialog agents (i.e. chatbots) aim to produce varying and
engaging conversations with a user; however, they typically exhibit either
inconsistent personality across conversations or the average personality of all
users. This paper addresses these issues by controlling an agent's persona upon
generation via conditioning on prior conversations of a target actor. In doing
so, we are able to utilize more abstract patterns within a person's speech and
better emulate them in generated responses. This work introduces the Generative
Conversation Control model, an augmented and fine-tuned GPT-2 language model
that conditions on past reference conversations to probabilistically model
multi-turn conversations in the actor's persona. We introduce an accompanying
data collection procedure to obtain 10.3M conversations from 6 months worth of
Reddit comments. We demonstrate that scaling model sizes from 117M to 8.3B
parameters yields an improvement from 23.14 to 13.14 perplexity on 1.7M held
out Reddit conversations. Increasing model scale yielded similar improvements
in human evaluations that measure preference of model samples to the held out
target distribution in terms of realism (31% increased to 37% preference),
style matching (37% to 42%), grammar and content quality (29% to 42%), and
conversation coherency (32% to 40%). We find that conditionally modeling past
conversations improves perplexity by 0.47 in automatic evaluations. Through
human trials we identify positive trends between conditional modeling and style
matching and outline steps to further improve persona control.
