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=== Transformers ===
Similar to how [[Transformer (machine learning model)|Transformer models]] influenced paraphrase generation, their application in identifying paraphrases showed great success. Models such as BERT can be adapted with a [[binary classification]] layer and trained end-to-end on identification tasks.<ref>{{Cite journal |last1=Devlin |first1=Jacob |last2=Chang |first2=Ming-Wei |last3=Lee |first3=Kenton |last4=Toutanova |first4=Kristina |date=2019 |title=[No title found] |url=http://aclweb.org/anthology/N19-1423 |journal=Proceedings of the 2019 Conference of the North |language=en |___location=Minneapolis, Minnesota |publisher=Association for Computational Linguistics |pages=4171–4186 |doi=10.18653/v1/N19-1423|s2cid=52967399 }}</ref><ref>{{Citation |last1=Wahle |first1=Jan Philip |title=Identifying Machine-Paraphrased Plagiarism |date=2022 |url=https://link.springer.com/10.1007/978-3-030-96957-8_34 |work=Information for a Better World: Shaping the Global Future |volume=13192 |pages=393–413 |editor-last=Smits |editor-first=Malte |place=Cham |publisher=Springer International Publishing |language=en |doi=10.1007/978-3-030-96957-8_34 |isbn=978-3-030-96956-1 |access-date=2022-10-06 |last2=Ruas |first2=Terry |last3=Foltýnek |first3=Tomáš |last4=Meuschke |first4=Norman |last5=Gipp |first5=Bela|s2cid=232307572 }}</ref> Transformers achieve strong results when transferring between domains and paraphrasing techniques compared to more traditional machine learning methods such as [[logistic regression]]. Other successful methods based on the Transformer architecture include using [[Adversarial machine learning|adversarial learning]] and [[Meta
== Evaluation ==
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