Selecting an appropriate response from many candidates given the utterances
in a multi-turn dialogue is the key problem for a retrieval-based dialogue
system. Existing work formalizes the task as matching between the utterances
and a candidate and uses the cross-entropy loss in learning of the model. This
paper applies contrastive learning to the problem by using the supervised
contrastive loss. In this way, the learned representations of positive examples
and representations of negative examples can be more distantly separated in the
embedding space, and the performance of matching can be enhanced. We further
develop a new method for supervised contrastive learning, referred to as
two-level supervised contrastive learning, and employ the method in response
selection in multi-turn dialogue. Our method exploits two techniques: sentence
token shuffling (STS) and sentence re-ordering (SR) for supervised contrastive
learning. Experimental results on three benchmark datasets demonstrate that the
proposed method significantly outperforms the contrastive learning baseline and
the state-of-the-art methods for the task.

该论文研究了在多轮对话中选择最适当的回应是一个检索型对话系统的关键问题，提出了一种基于受监督对比学习的新方法并应用于回应选取中，利用句子标记交换和句子重排序的技术，经过实验验证，该方法明显优于基线和目前各种方法。