Simpson's Paradox and the Accuracy-Fluency Tradeoff in Translation
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Abstract (arXiv)
A good translation should be faithful to the source and should respect the norms of the target language. We address a theoretical puzzle about the relationship between these objectives. On one hand, intuition and some prior work suggest that accuracy and fluency should trade off against each other, and that capturing every detail of the source can only be achieved at the cost of fluency. On the other hand, quality assessment researchers often suggest that accuracy and fluency are highly correlated and difficult for human raters to distinguish (Callison-Burch et al., 2007). We show that the tension between these views is an instance of Simpson's paradox, and that accuracy and fluency are positively correlated at the level of the corpus but trade off at the level of individual source segments. We further suggest that the relationship between accuracy and fluency is best evaluated at the segment (or sentence) level, and that the trade off between these dimensions has implications both for assessing translation quality and developing improved MT systems.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 9 of its 57 references that are in the map. First posted on arXiv 20 Feb 2024.
Builds on
9 of 57 references.
References · 9
- 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,290
- 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,196
- 2020Beyond English-Centric Multilingual Machine Translationcited by 559
- 2021Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translationcited by 333
- 2019Domain Robustness in Neural Machine Translationcited by 64
- 2019Simple and Effective Noisy Channel Modeling for Neural Machine Translationcited by 37
- 2018Fluency Over Adequacy: A Pilot Study in Measuring User Trust in Imperfect MTcited by 21
- 2016The Neural Noisy Channelcited by 19
- 2021Simpson's Bias in NLP Trainingcited by 3
Built on by
Citing papers per year: 2024: 3; 2025: 3; 2026: 2.
8 papers.
- 2024Speech is More Than Words: Do Speech-to-Text Translation Systems Leverage Prosody?cited by 13
- 2024Multi-Dimensional Machine Translation Evaluation: Model Evaluation and Resource for Koreancited by 4
- 2025You Cannot Feed Two Birds with One Score: the Accuracy-Naturalness Tradeoff in Translationcited by 4
- 2025MegaWika 2: A More Comprehensive Multilingual Collection of Articles and their Sourcescited by 3
- 2024A Probability--Quality Trade-off in Aligned Language Models and its Relation to Sampling Adaptorscited by 0
- 2025Toward Machine Interpreting: Lessons from Human Interpreting Studiescited by 0
- 2026Dialects of Translationese Shape Language Model Learningcited by 0
- 2026Translation from the Information Bottleneck Perspective: an Efficiency Analysis of Spatial Prepositions in Bitextscited by 0
- 2026Translation from the Information Bottleneck Perspective: an Efficiency Analysis of Spatial Prepositions in Bitexts20 Mar 2026
- 2026Dialects of Translationese Shape Language Model Learning18 Feb 2026
- 2025Toward Machine Interpreting: Lessons from Human Interpreting Studies11 Aug 2025
- 2025MegaWika 2: A More Comprehensive Multilingual Collection of Articles and their Sources5 Aug 2025
- 2025You Cannot Feed Two Birds with One Score: the Accuracy-Naturalness Tradeoff in Translation31 Mar 2025
- 2024Speech is More Than Words: Do Speech-to-Text Translation Systems Leverage Prosody?31 Oct 2024
- 2024A Probability--Quality Trade-off in Aligned Language Models and its Relation to Sampling Adaptors14 Jun 2024
- 2024Multi-Dimensional Machine Translation Evaluation: Model Evaluation and Resource for Korean19 Mar 2024