  Exemplar-Guided Paraphrase Generation (EGPG) aims to generate a target
sentence which conforms to the style of the given exemplar while encapsulating
the content information of the source sentence. In this paper, we propose a new
method with the goal of learning a better representation of the style andthe
content. This method is mainly motivated by the recent success of contrastive
learning which has demonstrated its power in unsupervised feature extraction
tasks. The idea is to design two contrastive losses with respect to the content
and the style by considering two problem characteristics during training. One
characteristic is that the target sentence shares the same content with the
source sentence, and the second characteristic is that the target sentence
shares the same style with the exemplar. These two contrastive losses are
incorporated into the general encoder-decoder paradigm. Experiments on two
datasets, namely QQP-Pos and ParaNMT, demonstrate the effectiveness of our
proposed constrastive losses.
