mlsanityData to 14 Oct 2026

It's not a Non-Issue: Negation as a Source of Error in Machine Translation

arXiv 2010.05432 ↗First posted 12 Oct 2020cs.CL cs.AIcited by 19

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Abstract (arXiv)

As machine translation (MT) systems progress at a rapid pace, questions of their adequacy linger. In this study we focus on negation, a universal, core property of human language that significantly affects the semantics of an utterance. We investigate whether translating negation is an issue for modern MT systems using 17 translation directions as test bed. Through thorough analysis, we find that indeed the presence of negation can significantly impact downstream quality, in some cases resulting in quality reductions of more than 60%. We also provide a linguistically motivated analysis that directly explains the majority of our findings. We release our annotations and code to replicate our analysis here: https://github.com/mosharafhossain/negation-mt.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Oct 2020.

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Citing papers per year: 2021: 5; 2022: 3; 2023: 2; 2024: 3; 2025: 1; 2026: 5.

19 papers.

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  1. 2021AI and the Everything in the Whole Wide World Benchmarkcited by 349
  2. 2024Learn "No" to Say "Yes" Better: Improving Vision-Language Models via Negationscited by 46
  3. 2022CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negationcited by 39
  4. 2021"I'm Not Mad": Commonsense Implications of Negation and Contradictioncited by 26
  5. 2024URIEL+: Enhancing Linguistic Inclusion and Usability in a Typological and Multilingual Knowledge Basecited by 21
  6. 2022On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlationscited by 19
  7. 2022MENLI: Robust Evaluation Metrics from Natural Language Inferencecited by 18
  8. 2021On the Limits of Minimal Pairs in Contrastive Evaluationcited by 15
  9. 2021Revisiting Negation in Neural Machine Translationcited by 6
  10. 2023Can You Follow Me? Testing Situational Understanding in ChatGPTcited by 4
  11. 2024The Minimum Information about CLinical Artificial Intelligence Checklist for Generative Modeling Research (MI-CLAIM-GEN)cited by 3
  12. 2023Applications and Challenges of Sentiment Analysis in Real-life Scenarioscited by 2
7 more
  1. 2025From No to Know: Taxonomy, Challenges, and Opportunities for Negation Understanding in Multimodal Foundation Modelscited by 1
  2. 2021Negation-Instance Based Evaluation of End-to-End Negation Resolutioncited by 0
  3. 2026Thunder-KoNUBench: A Corpus-Aligned Benchmark for Korean Negation Understandingcited by 0
  4. 2026Revisiting the Systematicity in Negation in the Era of In-Context Learningcited by 0
  5. 2026Evaluating Multilingual Sentence Embeddings for Translation Error Detection:An English--Greek Contrastive Studycited by 0
  6. 2026Not all Negation Cues are Equal: Affixal Negations Yield Better Negation Understandingcited by 0
  7. 2026The Ongiini-Eval-OW Benchmark: A Concept Paper for the Planned Benchmarking of Machine Translation and Large Language Models on Oshindonga and Oshikwanyamacited by 0