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  ; however, attempting to generate paraphrases from the model using 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 trade-off between semantic similarity and lexical /syntactic diversity between the input and output can be controlled at generation time. We conduct  human evaluation to compare our method to a paraphraser trained on a large English synthetic paraphrase database  and find that our model produces paraphrases that better preserve semantic meaning and grammatically , for the same level of lexical /syntactic diversity. Additional smaller human assessments demonstrate our approach also works in  non-English languages.
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.
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(Thompson and Post, 2020)
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the model using
such a model using standard
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trade-off between semantic similarity and lexical /syntactic
amount of lexical
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a
R
a
the
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ParaBank 2 (Hu et al., 2019c)
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model
method
R
semantic meaning and grammatically
meaning and are more gramatical
D
/syntactic
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two