mlsanityData to 7 Oct 2026

Simpson's Paradox and the Accuracy-Fluency Tradeoff in Translation

arXiv 2402.12690 ↗First posted 20 Feb 2024cs.CLcited by 8

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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

  1. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,290
  2. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,196
  3. 2020Beyond English-Centric Multilingual Machine Translationcited by 559
  4. 2021Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translationcited by 333
  5. 2019Domain Robustness in Neural Machine Translationcited by 64
  6. 2019Simple and Effective Noisy Channel Modeling for Neural Machine Translationcited by 37
  7. 2018Fluency Over Adequacy: A Pilot Study in Measuring User Trust in Imperfect MTcited by 21
  8. 2016The Neural Noisy Channelcited by 19
  9. 2021Simpson's Bias in NLP Trainingcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 2.

8 papers.