  Unsupervised paraphrase generation is a promising and important research
topic in natural language processing. We propose UPSA, a novel approach that
accomplishes Unsupervised Paraphrasing by Simulated Annealing. We model
paraphrase generation as an optimization problem and propose a sophisticated
objective function, involving semantic similarity, expression diversity, and
language fluency of paraphrases. Then, UPSA searches the sentence space towards
this objective by performing a sequence of local editing. Our method is
unsupervised and does not require parallel corpora for training, so it could be
easily applied to different domains. We evaluate our approach on a variety of
benchmark datasets, namely, Quora, Wikianswers, MSCOCO, and Twitter. Extensive
results show that UPSA achieves the state-of-the-art performance compared with
previous unsupervised methods in terms of both automatic and human evaluations.
Further, our approach outperforms most existing domain-adapted supervised
models, showing the generalizability of UPSA.
