# Quality expectations of machine translation

arXiv: https://arxiv.org/abs/1803.08409 · HTML: https://mlsanity.com/paper/1803.08409/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2018.

## Built on by (most cited first, 9 of 9)

- [Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference?](https://mlsanity.com/paper/2106.01045/) (2021)
- [Survey on reinforcement learning for language processing](https://mlsanity.com/paper/2104.05565/) (2021)
- [Undesirable Biases in NLP: Addressing Challenges of Measurement](https://mlsanity.com/paper/2211.13709/) (2022)
- [adaptNMT: an open-source, language-agnostic development environment for Neural Machine Translation](https://mlsanity.com/paper/2403.02367/) (2024)
- [A new approach to calculating BERTScore for automatic assessment of translation quality](https://mlsanity.com/paper/2203.05598/) (2022)
- [A Bayesian approach to translators' reliability assessment](https://mlsanity.com/paper/2203.07135/) (2022)
- [Applying Automated Machine Translation to Educational Video Courses](https://mlsanity.com/paper/2301.03141/) (2023)
- [Design of an Open-Source Architecture for Neural Machine Translation](https://mlsanity.com/paper/2403.03582/) (2024)
- [AI 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 domains](https://mlsanity.com/paper/2505.01560/) (2025)

## 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.