Editing Machine translation (section)

=== Neural MT ===
{{Main|Neural machine translation}}

A [[deep learning]]-based approach to MT, [[neural machine translation]] has made rapid progress in recent years. However, the current consensus is that the so-called human parity achieved is not real, being based wholly on limited domains, language pairs, and certain test benchmarks<ref>Antonio Toral, Sheila Castilho, Ke Hu, and Andy
Way. 2018. Attaining the unattainable? reassessing claims of human parity in neural machine translation. CoRR, abs/1808.10432.</ref> i.e., it lacks statistical significance power.<ref>{{Cite arXiv |eprint=1906.09833 |first1=Graham |last1=Yvette |first2=Haddow |last2=Barry |title=Translationese in Machine Translation Evaluation |date=2019 |last3=Koehn |first3=Philipp|class=cs.CL }}</ref>

Translations by neural MT tools like [[DeepL Translator]], which is thought to usually deliver the best machine translation results as of 2022, typically still need post-editing by a human.<ref>{{cite journal |last1=Katsnelson |first1=Alla |title=Poor English skills? New AIs help researchers to write better |journal=Nature |pages=208–209 |language=en |doi=10.1038/d41586-022-02767-9 |date=29 August 2022|volume=609 |issue=7925 |pmid=36038730 |bibcode=2022Natur.609..208K |s2cid=251931306 |doi-access=free }}</ref><ref>{{cite web |last1=Korab |first1=Petr |title=DeepL: An Exceptionally Magnificent Language Translator |url=https://towardsdatascience.com/deepl-an-exceptionally-magnificent-language-translator-78e86d8062d3 |website=Medium |access-date=9 January 2023 |language=en |date=18 February 2022}}</ref><ref>{{cite news |title=DeepL outperforms Google Translate – DW – 12/05/2018 |url=https://www.dw.com/en/deepl-cologne-based-startup-outperforms-google-translate/a-46581948 |access-date=9 January 2023 |work=Deutsche Welle |language=en}}</ref>

Instead of training specialized translation models on parallel datasets, one can also [[Prompt engineering|directly prompt]] generative [[large language model]]s like [[Generative pre-trained transformer|GPT]] to translate a text.<ref name="Hendy2023">{{cite arXiv |last1=Hendy |first1=Amr |last2=Abdelrehim |first2=Mohamed |last3=Sharaf |first3=Amr |last4=Raunak |first4=Vikas |last5=Gabr |first5=Mohamed |last6=Matsushita |first6=Hitokazu |last7=Kim |first7=Young Jin |last8=Afify |first8=Mohamed |last9=Awadalla |first9=Hany |title=How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation |date=2023-02-18 |eprint=2302.09210 |class=cs.CL}}</ref><ref>{{cite news |last1=Fadelli |first1=Ingrid |title=Study assesses the quality of AI literary translations by comparing them with human translations |url=https://techxplore.com/news/2022-11-quality-ai-literary-human.html |access-date=18 December 2022 |work=techxplore.com |language=en}}</ref><ref name="arxiv221014250">{{Cite arXiv|last1=Thai |first1=Katherine |last2=Karpinska |first2=Marzena |last3=Krishna |first3=Kalpesh |last4=Ray |first4=Bill |last5=Inghilleri |first5=Moira |last6=Wieting |first6=John |last7=Iyyer |first7=Mohit |title=Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature |date=25 October 2022|class=cs.CL |eprint=2210.14250 }}</ref> This approach is considered promising,<ref name="WMT2023">{{cite conference |last1=Kocmi |first1=Tom |last2=Avramidis |first2=Eleftherios |last3=Bawden |first3=Rachel |last4=Bojar |first4=Ondřej |last5=Dvorkovich |first5=Anton |last6=Federmann |first6=Christian |last7=Fishel |first7=Mark |last8=Freitag |first8=Markus |last9=Gowda |first9=Thamme |last10=Grundkiewicz |first10=Roman |last11=Haddow |first11=Barry |last12=Koehn |first12=Philipp |last13=Marie |first13=Benjamin |last14=Monz |first14=Christof |last15=Morishita |first15=Makoto |date=2023 |editor-last=Koehn |editor-first=Philipp |editor2-last=Haddow |editor2-first=Barry |editor3-last=Kocmi |editor3-first=Tom |editor4-last=Monz |editor4-first=Christof |title=Findings of the 2023 Conference on Machine Translation (WMT23): LLMs Are Here but Not Quite There Yet |url=https://aclanthology.org/2023.wmt-1.1 |journal=Proceedings of the Eighth Conference on Machine Translation |location=Singapore |publisher=Association for Computational Linguistics |pages=1–42 |doi=10.18653/v1/2023.wmt-1.1|doi-access=free }}</ref> but is still more resource-intensive than specialized translation models.