Quality expectations of machine translation
Not on the map. Why?
Abstract (arXiv)
Machine Translation (MT) is being deployed for a range of use-cases by millions of people on a daily basis. There should, therefore, be no doubt as to the utility of MT. However, not everyone is convinced that MT can be useful, especially as a productivity enhancer for human translators. In this chapter, I address this issue, describing how MT is currently deployed, how its output is evaluated and how this could be enhanced, especially as MT quality itself improves. Central to these issues is the acceptance that there is no longer a single 'gold standard' measure of quality, such that the situation in which MT is deployed needs to be borne in mind, especially with respect to the expected 'shelf-life' of the translation itself.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Mar 2018.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 1; 2024: 2; 2025: 1.
9 papers.
- 2021Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference?cited by 54
- 2021Survey on reinforcement learning for language processingcited by 31
- 2022Undesirable Biases in NLP: Addressing Challenges of Measurementcited by 4
- 2024adaptNMT: an open-source, language-agnostic development environment for Neural Machine Translationcited by 2
- 2022A new approach to calculating BERTScore for automatic assessment of translation qualitycited by 1
- 2022A Bayesian approach to translators' reliability assessmentcited by 0
- 2023Applying Automated Machine Translation to Educational Video Coursescited by 0
- 2024Design of an Open-Source Architecture for Neural Machine Translationcited by 0
- 2025AI agents may be worth the hype but not the resources (yet): An initial exploration of machine translation quality and costs in three language pairs in the legal and news domainscited by 0
- 2025AI agents may be worth the hype but not the resources (yet): An initial exploration of machine translation quality and costs in three language pairs in the legal and news domains2 May 2025
- 2024Design of an Open-Source Architecture for Neural Machine Translation6 Mar 2024
- 2024adaptNMT: an open-source, language-agnostic development environment for Neural Machine Translation4 Mar 2024
- 2023Applying Automated Machine Translation to Educational Video Courses9 Jan 2023
- 2022Undesirable Biases in NLP: Addressing Challenges of Measurement24 Nov 2022
- 2022A Bayesian approach to translators' reliability assessment14 Mar 2022
- 2022A new approach to calculating BERTScore for automatic assessment of translation quality10 Mar 2022
- 2021Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference?2 Jun 2021
- 2021Survey on reinforcement learning for language processing12 Apr 2021