Unsupervised Paraphrasing without Translation

Aurko Roy; David Grangier

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arXiv · 1905.12752

Unsupervised Paraphrasing without Translation

Aurko Roy·David Grangier

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Abstract

Paraphrasing exemplifies the ability to abstract semantic content from surface forms. Recent work on automatic paraphrasing is dominated by methods leveraging Machine Translation (MT) as an intermediate step. This contrasts with humans, who can paraphrase without being bilingual. This work proposes to learn paraphrasing models from an unlabeled monolingual corpus only. To that end, we propose a residual variant of vector-quantized variational auto-encoder. We compare with MT-based approaches on paraphrase identification, generation, and training augmentation. Monolingual paraphrasing outperforms unsupervised translation in all settings. Comparisons with supervised translation are more mixed: monolingual paraphrasing is interesting for identification and augmentation; supervised translation is superior for generation.

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Aurko Roy, David Grangier. 2019-05-29. Unsupervised Paraphrasing without Translation. https://arxiv.org/abs/1905.12752

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