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

arXiv: https://arxiv.org/abs/2010.05432 · HTML: https://mlsanity.com/paper/2010.05432/

## Key facts

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

## Built on by (most cited first, 19 of 19)

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- [Learn "No" to Say "Yes" Better: Improving Vision-Language Models via Negations](https://mlsanity.com/paper/2403.20312/) (2024)
- [CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation](https://mlsanity.com/paper/2211.00295/) (2022)
- ["I'm Not Mad": Commonsense Implications of Negation and Contradiction](https://mlsanity.com/paper/2104.06511/) (2021)
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- [On the Limits of Minimal Pairs in Contrastive Evaluation](https://mlsanity.com/paper/2109.07465/) (2021)
- [Revisiting Negation in Neural Machine Translation](https://mlsanity.com/paper/2107.12203/) (2021)
- [Can You Follow Me? Testing Situational Understanding in ChatGPT](https://mlsanity.com/paper/2310.16135/) (2023)
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- [Applications and Challenges of Sentiment Analysis in Real-life Scenarios](https://mlsanity.com/paper/2301.09912/) (2023)
- [From No to Know: Taxonomy, Challenges, and Opportunities for Negation Understanding in Multimodal Foundation Models](https://mlsanity.com/paper/2502.09645/) (2025)
- [Negation-Instance Based Evaluation of End-to-End Negation Resolution](https://mlsanity.com/paper/2109.10013/) (2021)
- [Thunder-KoNUBench: A Corpus-Aligned Benchmark for Korean Negation Understanding](https://mlsanity.com/paper/2601.04693/) (2026)
- [Revisiting the Systematicity in Negation in the Era of In-Context Learning](https://mlsanity.com/paper/2606.16867/) (2026)
- [Evaluating Multilingual Sentence Embeddings for Translation Error Detection:An English--Greek Contrastive Study](https://mlsanity.com/paper/2608.28776/) (2026)
- [Not all Negation Cues are Equal: Affixal Negations Yield Better Negation Understanding](https://mlsanity.com/paper/2609.13685/) (2026)
- [The Ongiini-Eval-OW Benchmark: A Concept Paper for the Planned Benchmarking of Machine Translation and Large Language Models on Oshindonga and Oshikwanyama](https://mlsanity.com/paper/2609.31727/) (2026)

## 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.