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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 |title=Proceedings of the 2019 Conference of the North |date=2019 |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 |url-access=subscription |doi-access=free }}</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 |arxiv=2103.11909 }}</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-learning (computer science)|meta-learning]].<ref>{{Cite book |last1=Nighojkar |first1=Animesh |last2=Licato |first2=John |title=Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) |chapter=Improving Paraphrase Detection with the Adversarial Paraphrasing Task |date=2021 |chapter-url=https://aclanthology.org/2021.acl-long.552 |language=en |___location=Online |publisher=Association for Computational Linguistics |pages=7106–7116 |doi=10.18653/v1/2021.acl-long.552|s2cid=235436269 |doi-access=free }}</ref><ref>{{Cite book |last1=Dopierre |first1=Thomas |last2=Gravier |first2=Christophe |last3=Logerais |first3=Wilfried |title=Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) |chapter=ProtAugment: Intent Detection Meta-Learning through Unsupervised Diverse Paraphrasing |date=2021 |chapter-url=https://aclanthology.org/2021.acl-long.191 |language=en |___location=Online |publisher=Association for Computational Linguistics |pages=2454–2466 |doi=10.18653/v1/2021.acl-long.191|s2cid=236460333 |doi-access=free }}</ref>
== Evaluation ==
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