# Translationese as a Language in "Multilingual" NMT

arXiv: https://arxiv.org/abs/1911.03823 · HTML: https://mlsanity.com/paper/1911.03823/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2019.

## Built on by (most cited first, 30 of 36)

- [Tower: An Open Multilingual Large Language Model for Translation-Related Tasks](https://mlsanity.com/paper/2402.17733/) (2024)
- [Scaling Laws for Neural Machine Translation](https://mlsanity.com/paper/2109.07740/) (2021)
- [Frustratingly Easy Label Projection for Cross-lingual Transfer](https://mlsanity.com/paper/2211.15613/) (2022)
- [Iterative Translation Refinement with Large Language Models](https://mlsanity.com/paper/2306.03856/) (2023)
- [Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP](https://mlsanity.com/paper/2110.03618/) (2021)
- [Zero-Shot Cross-lingual Semantic Parsing](https://mlsanity.com/paper/2104.07554/) (2021)
- [SEA-HELM: Southeast Asian Holistic Evaluation of Language Models](https://mlsanity.com/paper/2502.14301/) (2025)
- [BHASA: A Holistic Southeast Asian Linguistic and Cultural Evaluation Suite for Large Language Models](https://mlsanity.com/paper/2309.06085/) (2023)
- [Bandits Don't Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits](https://mlsanity.com/paper/2110.06997/) (2021)
- [Understanding Translationese in Cross-Lingual Summarization](https://mlsanity.com/paper/2212.07220/) (2022)
- [Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification](https://mlsanity.com/paper/2109.07604/) (2021)
- [On the Language Coverage Bias for Neural Machine Translation](https://mlsanity.com/paper/2106.03297/) (2021)
- [Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance](https://mlsanity.com/paper/2205.02293/) (2022)
- [Lost in Literalism: How Supervised Training Shapes Translationese in LLMs](https://mlsanity.com/paper/2503.04369/) (2025)
- [To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translation](https://mlsanity.com/paper/2401.01419/) (2024)
- [MT-PATCHER: Selective and Extendable Knowledge Distillation from Large Language Models for Machine Translation](https://mlsanity.com/paper/2403.09522/) (2024)
- [Milco: Learned Sparse Retrieval Across Languages via a Multilingual Connector](https://mlsanity.com/paper/2510.00671/) (2025)
- [Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction](https://mlsanity.com/paper/2306.06804/) (2023)
- [MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset](https://mlsanity.com/paper/2305.04582/) (2023)
- [On Evaluating Multilingual Compositional Generalization with Translated Datasets](https://mlsanity.com/paper/2306.11420/) (2023)
- [Explaining Translationese: why are Neural Classifiers Better and what do they Learn?](https://mlsanity.com/paper/2210.13391/) (2022)
- [Batayan: A Filipino NLP benchmark for evaluating Large Language Models](https://mlsanity.com/paper/2502.14911/) (2025)
- [On Systematic Style Differences between Unsupervised and Supervised MT and an Application for High-Resource Machine Translation](https://mlsanity.com/paper/2106.15818/) (2021)
- [HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints](https://mlsanity.com/paper/2109.04443/) (2021)
- [Towards Debiasing Translation Artifacts](https://mlsanity.com/paper/2205.08001/) (2022)
- [Original or Translated? On the Use of Parallel Data for Translation Quality Estimation](https://mlsanity.com/paper/2212.10257/) (2022)
- [Text Style Transfer Back-Translation](https://mlsanity.com/paper/2306.01318/) (2023)
- [Mitigating Translationese in Low-resource Languages: The Storyboard Approach](https://mlsanity.com/paper/2407.10152/) (2024)
- [Treasure Hunt: Real-time Targeting of the Long Tail using Training-Time Markers](https://mlsanity.com/paper/2506.14702/) (2025)
- [Synthetic Document Question Answering in Hungarian](https://mlsanity.com/paper/2505.23008/) (2025)

## Abstract (arXiv)

Machine translation has an undesirable propensity to produce "translationese" artifacts, which can lead to higher BLEU scores while being liked less by human raters. Motivated by this, we model translationese and original (i.e. natural) text as separate languages in a multilingual model, and pose the question: can we perform zero-shot translation between original source text and original target text? There is no data with original source and original target, so we train sentence-level classifiers to distinguish translationese from original target text, and use this classifier to tag the training data for an NMT model. Using this technique we bias the model to produce more natural outputs at test time, yielding gains in human evaluation scores on both accuracy and fluency. Additionally, we demonstrate that it is possible to bias the model to produce translationese and game the BLEU score, increasing it while decreasing human-rated quality. We analyze these models using metrics to measure the degree of translationese in the output, and present an analysis of the capriciousness of heuristically-based train-data tagging.