# Improving a Multi-Source Neural Machine Translation Model with Corpus Extension for Low-Resource Languages

arXiv: https://arxiv.org/abs/1709.08898 · HTML: https://mlsanity.com/paper/1709.08898/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2017.

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

- [Contrastive Learning for Many-to-many Multilingual Neural Machine Translation](https://mlsanity.com/paper/2105.09501/) (2021)
- [Back-translation for Large-Scale Multilingual Machine Translation](https://mlsanity.com/paper/2109.08712/) (2021)
- [Revealing the Parallel Multilingual Learning within Large Language Models](https://mlsanity.com/paper/2403.09073/) (2024)
- [Pivot Language for Low-Resource Machine Translation](https://mlsanity.com/paper/2505.14553/) (2025)

## Abstract (arXiv)

In machine translation, we often try to collect resources to improve performance. However, most of the language pairs, such as Korean-Arabic and Korean-Vietnamese, do not have enough resources to train machine translation systems. In this paper, we propose the use of synthetic methods for extending a low-resource corpus and apply it to a multi-source neural machine translation model. We showed the improvement of machine translation performance through corpus extension using the synthetic method. We specifically focused on how to create source sentences that can make better target sentences, including the use of synthetic methods. We found that the corpus extension could also improve the performance of multi-source neural machine translation. We showed the corpus extension and multi-source model to be efficient methods for a low-resource language pair. Furthermore, when both methods were used together, we found better machine translation performance.