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Paraphrase generation is a longstanding NLP task that has diverseapplications for downstream NLP tasks. However, the effectiveness of existingefforts predominantly relies on large amounts of golden labeled data. Thoughunsupervised endeavors have been proposed to address this issue, they may failto generate meaningful paraphrases due to the lack of supervision signals. Inthis work, we go beyond the existing paradigms and propose a novel approach togenerate high-quality paraphrases with weak supervision data. Specifically, wetackle the weakly-supervised paraphrase generation problem by: (1) obtainingabundant weakly-labeled parallel sentences via retrieval-based pseudoparaphrase expansion; and (2) developing a meta-learning framework toprogressively select valuable samples for fine-tuning a pre-trained languagemodel, i.e., BART, on the sentential paraphrasing task. We demonstrate that ourapproach achieves significant improvements over existing unsupervisedapproaches, and is even comparable in performance with supervisedstate-of-the-arts.

Learning to Selectively Learn for Weakly-supervised Paraphrase Generation