# Boosting Neural Machine Translation

arXiv: https://arxiv.org/abs/1612.06138 · HTML: https://mlsanity.com/paper/1612.06138/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2016.

## Built on by (most cited first, 4 of 4)

- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [Exploiting Curriculum Learning in Unsupervised Neural Machine Translation](https://mlsanity.com/paper/2109.11177/) (2021)
- [Separating the Wheat from the Chaff with BREAD: An open-source benchmark and metrics to detect redundancy in text](https://mlsanity.com/paper/2311.06440/) (2023)
- [Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain Adaptation in Neural Machine Translation](https://mlsanity.com/paper/2309.02640/) (2023)

## Abstract (arXiv)

Training efficiency is one of the main problems for Neural Machine Translation (NMT). Deep networks need for very large data as well as many training iterations to achieve state-of-the-art performance. This results in very high computation cost, slowing down research and industrialisation. In this paper, we propose to alleviate this problem with several training methods based on data boosting and bootstrap with no modifications to the neural network. It imitates the learning process of humans, which typically spend more time when learning "difficult" concepts than easier ones. We experiment on an English-French translation task showing accuracy improvements of up to 1.63 BLEU while saving 20% of training time.