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.

本文将三个先前提出的可控生成结构应用于开放领域对话生成，控制生成的样式以匹配大约 200 种可能的样式之一，并比较它们各自的性能和权衡，展示它们如何提供对现有对话数据集的深入洞察，以及如何生成各种样式的会话回复