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。

什么是良好的对话？可控属性如何影响人类判断

What makes a good conversation? How controllable attributes affect human  judgments

Sequence-to-Sequence (seq2seq) models have become overwhelmingly popular in
building end-to-end trainable dialogue systems. Though highly efficient in
learning the backbone of human-computer communications, they suffer from the
problem of strongly favoring short generic responses. In this paper, we argue
that a good response should smoothly connect both the preceding dialogue
history and the following conversations. We strengthen this connection through
mutual information maximization. To sidestep the non-differentiability of
discrete natural language tokens, we introduce an auxiliary continuous code
space and map such code space to a learnable prior distribution for generation
purpose. Experiments on two dialogue datasets validate the effectiveness of our
model, where the generated responses are closely related to the dialogue
context and lead to more interactive conversations.

本文介绍一种利用序列到序列（seq2seq）模型在对话系统中生成更加相关且导致更多互动对话的响应的方法，通过最大化互信息并介绍连续的编码空间，将其映射到一种可学习的先验分布进行生成目的，实验证明了该模型的有效性。