# Neural machine translation, corpus and frugality

arXiv: https://arxiv.org/abs/2101.10650 · HTML: https://mlsanity.com/paper/2101.10650/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 26 Jan 2021.

## Builds on (most cited first, 2 of 2)

- [Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges](https://mlsanity.com/paper/1907.05019/) (2019)
- [Massively Multilingual Neural Machine Translation](https://mlsanity.com/paper/1903.00089/) (2019)

## Built on by (most cited first, 2 of 2)

- [CJaFr-v3 : A Freely Available Filtered Japanese-French Aligned Corpus](https://mlsanity.com/paper/2208.13170/) (2022)
- [A Snapshot into the Possibility of Video Game Machine Translation](https://mlsanity.com/paper/2209.08827/) (2022)

## Abstract (arXiv)

In machine translation field, in both academia and industry, there is a growing interest in increasingly powerful systems, using corpora of several hundred million to several billion examples. These systems represent the state-of-the-art. Here we defend the idea of developing in parallel <<frugal>> bilingual translation systems, trained with relatively small corpora. Based on the observation of a standard human professional translator, we estimate that the corpora should be composed at maximum of a monolingual sub-corpus of 75 million examples for the source language, a second monolingual sub-corpus of 6 million examples for the target language, and an aligned bilingual sub-corpus of 6 million bi-examples. A less desirable alternative would be an aligned bilingual corpus of 47.5 million bi-examples.