Abstract
Paraphrasing exists at different granularity levels, such as lexical level, phrasal level and sentential level. This paper presents Decomposable Neural Paraphrase Generator (DNPG), a
Transformer-based model that can learn and
generate paraphrases of a sentence at different levels of granularity in a disentangled
way. Specifically, the model is composed of
multiple encoders and decoders with different structures, each of which corresponds to
a specific granularity. The empirical study
shows that the decomposition mechanism of
DNPG makes paraphrase generation more interpretable and controllable. Based on DNPG,
we further develop an unsupervised domain
adaptation method for paraphrase generation.
Experimental results show that the proposed
model achieves competitive in-domain performance compared to the state-of-the-art neural
models, and significantly better performance
when adapting to a new domain