Talk:Machine translation

does anyone else think this is a joke?

Although there is no system that provides the holy-grail of "Fully automatic high quality machine translation" (FAHQMT), many systems provide reasonable output.

FAHQ MT? Who came up with this acronym? Isn't there a less dramatic and confusing way to summarize that machine translation isn't as high quality as human translation, but produces practical results? - enjone

I think that the actual acronym is FAHQT (Fully Automated High Quality Translation) and dates back to 1950s. Atleast Yehoshua Bar-Hillel uses this acronym on his 1960 article

The Present Status of Automatic Translation of Languages on which he refers to his prior work at 1952 on the first machine translation conference. I couldn't get my hands on that article anywhere, but I'd wager that it is where the term was first coined as suggested by John Hutchins on his 2001 article Machine translation over fifty years (and number of other articles by him). —Preceding unsigned comment added by 78.27.78.126 (talk) 11:57, 31 October 2009 (UTC)

http://www.isi.edu/natural- 2806:263:8401:751:981E:5E5C:403E:E860 (talk) 09:08, 6 July 2024 (UTC)

The first sentence: Machine translation ... is a sub-field of computational linguistics ... - is that not secondary to what it actually is: automatic translation of texts from one language into another language by a computer program? Wammes Waggel (talk) 12:32, 12 April 2023 (UTC)

If you all don't mind, I am going to add a new subsection in the Applications section about MT use in law, since it is being investigated and I feel that it is significant due to the importance and difficulty of accurate translating in the legal sector. I'm also going to add a little bit to the medical applications subsection that goes into depth about research regarding MT use.

Arkenly (talk) 00:27, 6 December 2023 (UTC)

Hello everyone, I would like to propose the addition of a new subsection titled Adaptive Machine Translation to the existing Machine Translation article. This is a growing area of research and application within the field of MT, and I believe it deserves its own brief section within the article to reflect recent academic and commercial developments. Adaptive Machine Translation (AMT) refers to systems that learn from human feedback in real time or near real time, allowing them to adapt translations based on user corrections or domain-specific usage. Unlike static NMT systems, adaptive systems improve continuously during translation tasks. This approach is increasingly adopted both in academic research and in commercial tools Here is a draft of the proposed text for inclusion:

Adaptive Machine Translation (AMT) refers to systems capable of dynamically learning from human feedback during or immediately after the translation process. Unlike static Neural Machine Translation (NMT) models, which rely on fixed training data, adaptive systems incorporate user corrections in real time to refine future translations without full model retraining.

This adaptability is particularly beneficial in scenarios where domain-specific terminology or user preferences evolve rapidly, such as technical documentation, customer support, or e-commerce localization. AMT systems can operate in an online learning setting, where translations improve progressively within a session or across sessions.

Adaptive techniques are also being explored in interactive translation environments and are increasingly integrated into translation technology training programs.

References would be properly formatted and added to the reference section. I'm happy to refine the draft based on feedback.

1. Peris, C., Domingo, M., & Casacuberta, F. (2017).Interactive Neural Machine Translation Computer Speech & Language, 45, 201–220.
2. Farajian, M. A., Turchi, M., Negri, M., & Federico, M. (2017). Multi-Domain Neural Machine Translation through Unsupervised Adaptation. Proceedings of WMT 2017. Bentivogli, L., Bisazza, A., Cettolo, M., & Federico, M. (2016). Neural versus phrase-based machine translation quality: a case study. EMNLP 2016.
3. Knowles, R., & Koehn, P. (2016). Neural Interactive Translation Prediction. IWSLT 2016.
4. Kenny, D. (2020). Translation Technology in Translator Training. Routledge
5. O’Hagan, M. (Ed.). (2020). The Routledge Handbook of Translation and Technology. Routledge.

Giovannitech (talk) 15:49, 21 May 2025 (UTC)