Zero-Shot Paraphrase Generation with Multilingual Language Models

Understand

Leveraging multilingual parallel texts to automatically generate paraphrases has drawn much attention as size of high-quality paraphrase corpus is limited.

• Round-trip translation, also known as the pivoting method, is a typical approach to this end.
• However, we notice that the pivoting process involves multiple machine translation models and is likely to incur semantic drift during the two-step translations.
• In this paper, inspired by the Transformer-based language models, we propose a simple and unified paraphrasing model, which is purely trained on multilingual parallel data and can conduct zero-shot paraphrase generation in one step.