A good conversation requires balance -- between simplicity and detail;
staying on topic and changing it; asking questions and answering them. Although
dialogue agents are commonly evaluated via human judgments of overall quality,
the relationship between quality and these individual factors is less
well-studied. In this work, we examine two controllable neural text generation
methods, conditional training and weighted decoding, in order to control four
important attributes for chitchat dialogue: repetition, specificity,
response-relatedness and question-asking. We conduct a large-scale human
evaluation to measure the effect of these control parameters on multi-turn
interactive conversations on the PersonaChat task. We provide a detailed
analysis of their relationship to high-level aspects of conversation, and show
that by controlling combinations of these variables our models obtain clear
improvements in human quality judgments.

本研究针对通俗聊天对话，通过调整可控的神经文本生成参数（条件训练和加权解码），控制重复、特定性、关联性和提问行为等四个重要特征，进行大规模人类评测，结果显示这些参数的不同组合能明显提升整体品质。