# Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation

arXiv: https://arxiv.org/abs/1906.12068 · HTML: https://mlsanity.com/paper/1906.12068/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2019.

## Built on by (most cited first, 30 of 45)

- [Gender Bias in Machine Translation](https://mlsanity.com/paper/2104.06001/) (2021)
- [Bias in Large Language Models: Origin, Evaluation, and Mitigation](https://mlsanity.com/paper/2411.10915/) (2024)
- [Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation](https://mlsanity.com/paper/2102.00287/) (2021)
- [EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling](https://mlsanity.com/paper/2403.14541/) (2024)
- [First the worst: Finding better gender translations during beam search](https://mlsanity.com/paper/2104.07429/) (2021)
- [Investigating Failures of Automatic Translation in the Case of Unambiguous Gender](https://mlsanity.com/paper/2104.07838/) (2021)
- [Machine Translation from Signed to Spoken Languages: State of the Art and Challenges](https://mlsanity.com/paper/2202.03086/) (2022)
- [On the Limits of Minimal Pairs in Contrastive Evaluation](https://mlsanity.com/paper/2109.07465/) (2021)
- [How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation](https://mlsanity.com/paper/2105.13782/) (2021)
- [Frequency-Aware Contrastive Learning for Neural Machine Translation](https://mlsanity.com/paper/2112.14484/) (2021)
- [The Impact of Post-editing and Machine Translation on Creativity and Reading Experience](https://mlsanity.com/paper/2101.06125/) (2021)
- [Mixup Decoding for Diverse Machine Translation](https://mlsanity.com/paper/2109.03402/) (2021)
- [To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translation](https://mlsanity.com/paper/2401.01419/) (2024)
- [A Survey on Zero Pronoun Translation](https://mlsanity.com/paper/2305.10196/) (2023)
- [Are We Paying Attention to Her? Investigating Gender Disambiguation and Attention in Machine Translation](https://mlsanity.com/paper/2505.08546/) (2025)
- [Translating the Unseen? Yoruba-English MT in Low-Resource, Morphologically-Unmarked Settings](https://mlsanity.com/paper/2103.04225/) (2021)
- [Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation](https://mlsanity.com/paper/2105.12523/) (2021)
- [Exploring Diversity in Back Translation for Low-Resource Machine Translation](https://mlsanity.com/paper/2206.00564/) (2022)
- [Towards Tailored Recovery of Lexical Diversity in Literary Machine Translation](https://mlsanity.com/paper/2408.17308/) (2024)
- [Voice, Bias, and Coreference: An Interpretability Study of Gender in Speech Translation](https://mlsanity.com/paper/2511.21517/) (2025)
- [Automatic Classification of Human Translation and Machine Translation: A Study from the Perspective of Lexical Diversity](https://mlsanity.com/paper/2105.04616/) (2021)
- [On Systematic Style Differences between Unsupervised and Supervised MT and an Application for High-Resource Machine Translation](https://mlsanity.com/paper/2106.15818/) (2021)
- [Fair Comparison: Quantifying Variance in Resultsfor Fine-grained Visual Categorization](https://mlsanity.com/paper/2109.03156/) (2021)
- [Gender Bias in Machine Translation and The Era of Large Language Models](https://mlsanity.com/paper/2401.10016/) (2024)
- [BitextEdit: Automatic Bitext Editing for Improved Low-Resource Machine Translation](https://mlsanity.com/paper/2111.06787/) (2021)
- [Machine Translation Models are Zero-Shot Detectors of Translation Direction](https://mlsanity.com/paper/2401.06769/) (2024)
- [CKnowEdit: A New Chinese Knowledge Editing Dataset for Linguistics, Facts, and Logic Error Correction in LLMs](https://mlsanity.com/paper/2409.05806/) (2024)
- [Decoding Machine Translationese in English-Chinese News: LLMs vs. NMTs](https://mlsanity.com/paper/2506.22050/) (2025)
- [NeoBabel: A Multilingual Open Tower for Visual Generation](https://mlsanity.com/paper/2507.06137/) (2025)
- [Enhancing Effectiveness and Robustness in a Low-Resource Regime via Decision-Boundary-aware Data Augmentation](https://mlsanity.com/paper/2403.15512/) (2024)

## Abstract (arXiv)

This work presents an empirical approach to quantifying the loss of lexical richness in Machine Translation (MT) systems compared to Human Translation (HT). Our experiments show how current MT systems indeed fail to render the lexical diversity of human generated or translated text. The inability of MT systems to generate diverse outputs and its tendency to exacerbate already frequent patterns while ignoring less frequent ones, might be the underlying cause for, among others, the currently heavily debated issues related to gender biased output. Can we indeed, aside from biased data, talk about an algorithm that exacerbates seen biases?