# Translation Errors Significantly Impact Low-Resource Languages in Cross-Lingual Learning

arXiv: https://arxiv.org/abs/2402.02080 · HTML: https://mlsanity.com/paper/2402.02080/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 28 references that are in the map.
- First posted on arXiv 3 Feb 2024.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [XNLI: Evaluating Cross-lingual Sentence Representations](https://mlsanity.com/paper/1809.05053/) (2018)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://mlsanity.com/paper/1910.07475/) (2019)
- [PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification](https://mlsanity.com/paper/1908.11828/) (2019)
- [Word Alignment by Fine-tuning Embeddings on Parallel Corpora](https://mlsanity.com/paper/2101.08231/) (2021)
- [XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation](https://mlsanity.com/paper/2104.07412/) (2021)
- [Translation Artifacts in Cross-lingual Transfer Learning](https://mlsanity.com/paper/2004.04721/) (2020)
- [Revisiting Machine Translation for Cross-lingual Classification](https://mlsanity.com/paper/2305.14240/) (2023)
- [XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages](https://mlsanity.com/paper/2305.11938/) (2023)

## Built on by (most cited first, 9 of 9)

- [Mangosteen: An Open Thai Corpus for Language Model Pretraining](https://mlsanity.com/paper/2507.14664/) (2025)
- [How and Where to Translate? The Impact of Translation Strategies in Cross-lingual LLM Prompting](https://mlsanity.com/paper/2507.22923/) (2025)
- [Extending Item Response Theory for Efficient and Meaningful Multilingual Evaluation](https://mlsanity.com/paper/2606.15643/) (2026)
- [Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders](https://mlsanity.com/paper/2507.18918/) (2025)
- [Quantifying the Impact of Translation Errors on Multilingual LLM Evaluation](https://mlsanity.com/paper/2605.24904/) (2026)
- [Training Language Models with homotokens Leads to Delayed Overfitting](https://mlsanity.com/paper/2601.02867/) (2026)
- [BETA-Labeling for Multilingual Dataset Construction in Low-Resource IR](https://mlsanity.com/paper/2602.14488/) (2026)
- [Is Textual Similarity Invariant under Machine Translation? Evidence Based on the Political Manifesto Corpus](https://mlsanity.com/paper/2605.00618/) (2026)
- [SEA-NLI: Natural Language Inference as a Lens into Southeast Asian Cultural Understanding](https://mlsanity.com/paper/2606.03284/) (2026)

## Abstract (arXiv)

Popular benchmarks (e.g., XNLI) used to evaluate cross-lingual language understanding consist of parallel versions of English evaluation sets in multiple target languages created with the help of professional translators. When creating such parallel data, it is critical to ensure high-quality translations for all target languages for an accurate characterization of cross-lingual transfer. In this work, we find that translation inconsistencies do exist and interestingly they disproportionally impact low-resource languages in XNLI. To identify such inconsistencies, we propose measuring the gap in performance between zero-shot evaluations on the human-translated and machine-translated target text across multiple target languages; relatively large gaps are indicative of translation errors. We also corroborate that translation errors exist for two target languages, namely Hindi and Urdu, by doing a manual reannotation of human-translated test instances in these two languages and finding poor agreement with the original English labels these instances were supposed to inherit.