Recent work has shown that a multilingual neural machine translation (NMT)
model can be used to judge how well a sentence paraphrases another sentence in
the same language (Thompson and Post, 2020); however, attempting to generate
paraphrases from such a model using standard beam search produces trivial
copies or near copies. We introduce a simple paraphrase generation algorithm
which discourages the production of n-grams that are present in the input. Our
approach enables paraphrase generation in many languages from a single
multilingual NMT model. Furthermore, the amount of lexical diversity between
the input and output can be controlled at generation time. We conduct a human
evaluation to compare our method to a paraphraser trained on the large English
synthetic paraphrase database ParaBank 2 (Hu et al., 2019c) and find that our
method produces paraphrases that better preserve meaning and are more
gramatical, for the same level of lexical diversity. Additional smaller human
assessments demonstrate our approach also works in two non-English languages.

通过引入简单的改进算法，不鼓励生成输入中已有的 N-grams，利用多语言神经机器翻译模型生成同义句，控制输入和输出之间的词汇多样性，人工评估表明，该方法比基于 ParaBank 2 的同义句生成器更好地维护意义和语法正确性，并且在两种非英语语言上也同样有效。