# The Comparison of Translationese in Machine Translation and Human Transation in terms of Translation Relations

arXiv: https://arxiv.org/abs/2404.08661 · HTML: https://mlsanity.com/paper/2404.08661/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 126 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 16 of 16)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Six Challenges for Neural Machine Translation](https://mlsanity.com/paper/1706.03872/) (2017)
- [Achieving Human Parity on Automatic Chinese to English News Translation](https://mlsanity.com/paper/1803.05567/) (2018)
- [Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation](https://mlsanity.com/paper/1808.07048/) (2018)
- [Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation](https://mlsanity.com/paper/2102.00287/) (2021)
- [A Survey of Deep Learning Techniques for Neural Machine Translation](https://mlsanity.com/paper/2002.07526/) (2020)
- [Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation](https://mlsanity.com/paper/1906.12068/) (2019)
- [A Challenge Set Approach to Evaluating Machine Translation](https://mlsanity.com/paper/1704.07431/) (2017)
- [Examining the Tip of the Iceberg: A Data Set for Idiom Translation](https://mlsanity.com/paper/1802.04681/) (2018)
- [Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation](https://mlsanity.com/paper/2205.15301/) (2022)
- [Reassessing Claims of Human Parity and Super-Human Performance in Machine Translation at WMT 2019](https://mlsanity.com/paper/2005.05738/) (2020)
- [Evaluating Machine Translation Performance on Chinese Idioms with a Blacklist Method](https://mlsanity.com/paper/1711.07646/) (2017)
- [A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions](https://mlsanity.com/paper/1701.02901/) (2017)
- [Automatic Classification of Human Translation and Machine Translation: A Study from the Perspective of Lexical Diversity](https://mlsanity.com/paper/2105.04616/) (2021)
- [Detecting Syntactic Features of Translated Chinese](https://mlsanity.com/paper/1804.08756/) (2018)

## Built on by (most cited first, 1 of 1)

- [Direct Image-to-Modern Vietnamese Translation of Han-Nom Manuscripts via Multimodal RLHF Preference Alignment](https://mlsanity.com/paper/2607.11434/) (2026)

## Abstract (arXiv)

This study explores the distinctions between neural machine translation (NMT) and human translation (HT) through the lens of translation relations. It benchmarks HT to assess the translation techniques produced by an NMT system and aims to address three key research questions: the differences in overall translation relations between NMT and HT, how each utilizes non-literal translation techniques, and the variations in factors influencing their use of specific non-literal techniques. The research employs two parallel corpora, each spanning nine genres with the same source texts with one translated by NMT and the other by humans. Translation relations in these corpora are manually annotated on aligned pairs, enabling a comparative analysis that draws on linguistic insights, including semantic and syntactic nuances such as hypernyms and alterations in part-of-speech tagging. The results indicate that NMT relies on literal translation significantly more than HT across genres. While NMT performs comparably to HT in employing syntactic non-literal translation techniques, it falls behind in semantic-level performance.