5 research outputs found
Unsupervised Paraphrasing via Deep Reinforcement Learning
Paraphrasing is expressing the meaning of an input sentence in different
wording while maintaining fluency (i.e., grammatical and syntactical
correctness). Most existing work on paraphrasing use supervised models that are
limited to specific domains (e.g., image captions). Such models can neither be
straightforwardly transferred to other domains nor generalize well, and
creating labeled training data for new domains is expensive and laborious. The
need for paraphrasing across different domains and the scarcity of labeled
training data in many such domains call for exploring unsupervised paraphrase
generation methods. We propose Progressive Unsupervised Paraphrasing (PUP): a
novel unsupervised paraphrase generation method based on deep reinforcement
learning (DRL). PUP uses a variational autoencoder (trained using a
non-parallel corpus) to generate a seed paraphrase that warm-starts the DRL
model. Then, PUP progressively tunes the seed paraphrase guided by our novel
reward function which combines semantic adequacy, language fluency, and
expression diversity measures to quantify the quality of the generated
paraphrases in each iteration without needing parallel sentences. Our extensive
experimental evaluation shows that PUP outperforms unsupervised
state-of-the-art paraphrasing techniques in terms of both automatic metrics and
user studies on four real datasets. We also show that PUP outperforms
domain-adapted supervised algorithms on several datasets. Our evaluation also
shows that PUP achieves a great trade-off between semantic similarity and
diversity of expression