Neural dialogue response generation has gained much popularity in recent
years. Maximum Likelihood Estimation (MLE) objective is widely adopted in
existing dialogue model learning. However, models trained with MLE objective
function are plagued by the low-diversity issue when it comes to the
open-domain conversational setting. Inspired by the observation that humans not
only learn from the positive signals but also benefit from correcting behaviors
of undesirable actions, in this work, we introduce contrastive learning into
dialogue generation, where the model explicitly perceives the difference
between the well-chosen positive and negative utterances. Specifically, we
employ a pretrained baseline model as a reference. During contrastive learning,
the target dialogue model is trained to give higher conditional probabilities
for the positive samples, and lower conditional probabilities for those
negative samples, compared to the reference model. To manage the multi-mapping
relations prevailed in human conversation, we augment contrastive dialogue
learning with group-wise dual sampling. Extensive experimental results show
that the proposed group-wise contrastive learning framework is suited for
training a wide range of neural dialogue generation models with very favorable
performance over the baseline training approaches.

本研究利用对比学习方法，在已有预训练模型为基础上，训练对话生成模型，提高其多样性和能力以应对开放领域会话问题，并提出一种适用于广泛对话生成模型的群内对比学习框架，实验结果表明该方法在性能上表现优异。