mlsanityData to 14 Oct 2026

Machine Translation Models are Zero-Shot Detectors of Translation Direction

arXiv 2401.06769 ↗First posted 12 Jan 2024cs.CLcited by 3

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Abstract (arXiv)

Detecting the translation direction of parallel text has applications for machine translation training and evaluation, but also has forensic applications such as resolving plagiarism or forgery allegations. In this work, we explore an unsupervised approach to translation direction detection based on the simple hypothesis that p(translation|original)>p(original|translation), motivated by the well-known simplification effect in translationese or machine-translationese. In experiments with massively multilingual machine translation models across 20 translation directions, we confirm the effectiveness of the approach for high-resource language pairs, achieving document-level accuracies of 82--96% for NMT-produced translations, and 60--81% for human translations, depending on the model used. Code and demo are available at https://github.com/ZurichNLP/translation-direction-detection

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 21 of its 42 references that are in the map. First posted on arXiv 12 Jan 2024.

Builds on

21 of 42 references.

References · 21

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  3. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  4. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,312
  5. 2020COMET: A Neural Framework for MT Evaluationcited by 1,033
  6. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 788
  7. 2020Beyond English-Centric Multilingual Machine Translationcited by 561
  8. 2021The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translationcited by 560
  9. 2021Quality at a Glance: An Audit of Web-Crawled Multilingual Datasetscited by 243
  10. 2022CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Taskcited by 207
  11. 2020Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasingcited by 115
  12. 2020TransQuest: Translation Quality Estimation with Cross-lingual Transformerscited by 59
9 more
  1. 2018Are All Languages Equally Hard to Language-Model?cited by 53
  2. 2019Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translationcited by 45
  3. 2018Dual Conditional Cross-Entropy Filtering of Noisy Parallel Corporacited by 40
  4. 2019Post-editese: an Exacerbated Translationesecited by 29
  5. 2023Escaping the sentence-level paradigm in machine translationcited by 27
  6. 2020It's Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Informationcited by 26
  7. 2022Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performancecited by 11
  8. 2022NMTScore: A Multilingual Analysis of Translation-based Text Similarity Measurescited by 6
  9. 2022DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decodingcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026BLUFF: Benchmarking the Detection of False and Synthetic Content across 58 Low-Resource Languagescited by 2
  2. 2026The Impact of Editorial Intervention on Detecting Native Language Tracescited by 0
  3. 2026ComplexityMT: Benchmarking the Interaction Between Text Complexity and Machine Translationcited by 0