  In this paper, we propose a new paradigm for paraphrase generation by
treating the task as unsupervised machine translation (UMT) based on the
assumption that there must be pairs of sentences expressing the same meaning in
a large-scale unlabeled monolingual corpus. The proposed paradigm first splits
a large unlabeled corpus into multiple clusters, and trains multiple UMT models
using pairs of these clusters. Then based on the paraphrase pairs produced by
these UMT models, a unified surrogate model can be trained to serve as the
final \sts model to generate paraphrases, which can be directly used for test
in the unsupervised setup, or be finetuned on labeled datasets in the
supervised setup. The proposed method offers merits over
machine-translation-based paraphrase generation methods, as it avoids reliance
on bilingual sentence pairs. It also allows human intervene with the model so
that more diverse paraphrases can be generated using different filtering
criteria. Extensive experiments on existing paraphrase dataset for both the
supervised and unsupervised setups demonstrate the effectiveness the proposed
paradigm.
