Unsupervised Disentanglement Learning Model for Exemplar-Guided Paraphrase Generation

Exemplar-guided paraphrase generation is the task of generating a paraphrase for a source sentence when given another exemplar sentence as syntactic guidance information. The target sentence must convey the semantics of the source sentence in surface form, which is the same as or similar to that of the exemplar sentence. The existing supervised learning methods rely on large-scale human-annotated supervised datasets, which are expensive and time-consuming to collect. To mitigate the need for human annotations, it is necessary to develop an unsupervised learning method for the exemplar-guided paraphrase generation task in other languages or domains that lack supervised datasets. This study proposes an
U
nsupervised
D
isentanglement
L
earning (
UDL
) model to solve the exemplar-guided paraphrase generation task by learning to disentangle the semantic and syntactic representations of a sentence and reconstruct the sentence with these disentangled representations. We investigate the difficulty of implementing the unsupervised learning scheme and design a scrambling module for our UDL model to address this difficulty. Experiments demonstrate that our UDL model achieves state-of-the-art performance among the tested unsupervised methods and is comparable to supervised learning methods that require no pretraining.