# Comparing Formulaic Language in Human and Machine Translation: Insight from a Parliamentary Corpus

arXiv: https://arxiv.org/abs/2206.10919 · HTML: https://mlsanity.com/paper/2206.10919/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 22 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [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)
- [Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training](https://mlsanity.com/paper/1911.03860/) (2019)
- [Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation](https://mlsanity.com/paper/1808.07048/) (2018)
- [Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation](https://mlsanity.com/paper/1906.12068/) (2019)
- [Using CollGram to Compare Formulaic Language in Human and Neural Machine Translation](https://mlsanity.com/paper/2107.03625/) (2021)

## Abstract (arXiv)

A recent study has shown that, compared to human translations, neural machine translations contain more strongly-associated formulaic sequences made of relatively high-frequency words, but far less strongly-associated formulaic sequences made of relatively rare words. These results were obtained on the basis of translations of quality newspaper articles in which human translations can be thought to be not very literal. The present study attempts to replicate this research using a parliamentary corpus. The text were translated from French to English by three well-known neural machine translation systems: DeepL, Google Translate and Microsoft Translator. The results confirm the observations on the news corpus, but the differences are less strong. They suggest that the use of text genres that usually result in more literal translations, such as parliamentary corpora, might be preferable when comparing human and machine translations. Regarding the differences between the three neural machine systems, it appears that Google translations contain fewer highly collocational bigrams, identified by the CollGram technique, than Deepl and Microsoft translations.