  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.
