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.
翻译:Exmplar-Guided Plasphone (EGPG) 旨在生成一个与给定示例风格相符的目标句,同时包罗出源句的内容信息。 在本文中,我们提出了一种新的方法,目的是学习更好的样式和内容的表述。这一方法主要受最近对比性学习的成功推动,这种学习在未受监督的特征提取任务中表现出了它的力量。 其想法是通过在培训中考虑两个问题特征来设计内容和风格方面的两个对比性损失。 其中一个特征是目标句的内容与源句相同,而第二个特征是目标句与源句相同。这两个对比性损失被纳入了普通编码-解密模式。关于两个数据集的实验,即 QP-Pos 和 ParaNMT, 显示了我们提议的控制性损失的有效性。