# A Survey of Methods to Leverage Monolingual Data in Low-resource Neural Machine Translation

arXiv: https://arxiv.org/abs/1910.00373 · HTML: https://mlsanity.com/paper/1910.00373/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2019.

## Built on by (most cited first, 5 of 5)

- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
- [Mergen: The First Manchu-Korean Machine Translation Model Trained on Augmented Data](https://mlsanity.com/paper/2311.17492/) (2023)
- [RISC: Generating Realistic Synthetic Bilingual Insurance Contract](https://mlsanity.com/paper/2304.04212/) (2023)
- [Language verY Rare for All](https://mlsanity.com/paper/2412.13924/) (2024)

## Abstract (arXiv)

Neural machine translation has become the state-of-the-art for language pairs with large parallel corpora. However, the quality of machine translation for low-resource languages leaves much to be desired. There are several approaches to mitigate this problem, such as transfer learning, semi-supervised and unsupervised learning techniques. In this paper, we review the existing methods, where the main idea is to exploit the power of monolingual data, which, compared to parallel, is usually easier to obtain and significantly greater in amount.