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.

本文提出了一种将改写生成任务视为无监督机器翻译的新方法，通过将大规模无标签单语语料库拆分成多个簇并使用这些簇的对训练多个 UMT 模型，然后基于这些 UMT 模型生成的改写语句对，可以训练出一个统一的代理模型，用于生成改写句子，该方法避免了对双语句对的依赖，同时也可以让人类干预模型，使用不同的过滤标准生成更多元的改写语句。在现有的改写数据集上进行的实验表明了该方法的有效性。