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Syst."],"published-print":{"date-parts":[[2023,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Automatic paraphrase generation is an essential task of natural language processing. However, due to the scarcity of paraphrase corpus in many languages, Chinese, for example, generating high-quality paraphrases in these languages is still challenging. Especially in domain paraphrasing, it is even more difficult to obtain in-domain paraphrase sentence pairs. In this paper, we propose a novel approach for domain-specific paraphrase generation in a zero-shot fashion. Our approach is based on a sequence-to-sequence architecture. The encoder uses a pre-trained multilingual autoencoder model, and the decoder uses a pre-trained monolingual autoregressive model. Because these two models are pre-trained separately, they have different representations for the same token. Thus, we call them unaligned pre-trained language models. We train the sequence-to-sequence model with an English-to-Chinese machine translation corpus. Then, by inputting a Chinese sentence into this model, it could surprisingly generate fluent and diverse Chinese paraphrases. Since the unaligned pre-trained language models have inconsistent understandings of the Chinese language, we believe that the Chinese paraphrasing is actually performed in a Chinese-to-Chinese translation manner. In addition, we collect a small-scale English-to-Chinese machine translation corpus in the domain of computer science. By fine-tuning with this domain-specific corpus, our model shows an excellent capability of domain-paraphrasing. Experiment results show that our approach significantly outperforms previous baselines regarding Relevance, Fluency, and Diversity.<\/jats:p>","DOI":"10.1007\/s40747-022-00820-8","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T09:07:50Z","timestamp":1659344870000},"page":"1097-1110","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Zero-shot domain paraphrase with unaligned pre-trained language models"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4013-3492","authenticated-orcid":false,"given":"Zheng","family":"Chen","sequence":"first","affiliation":[]},{"given":"Hu","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Jiankun","family":"Ren","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,1]]},"reference":[{"issue":"3","key":"820_CR1","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1162\/coli_a_00002","volume":"36","author":"N Madnani","year":"2010","unstructured":"Madnani N, Dorr BJ (2010) Generating phrasal and sentential paraphrases: a survey of data-driven methods. 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