# Multi-Source Neural Machine Translation with Data Augmentation

arXiv: https://arxiv.org/abs/1810.06826 · HTML: https://mlsanity.com/paper/1810.06826/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2018.

## Built on by (most cited first, 6 of 6)

- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [Revealing the Parallel Multilingual Learning within Large Language Models](https://mlsanity.com/paper/2403.09073/) (2024)
- [Crowdsourcing Parallel Corpus for English-Oromo Neural Machine Translation using Community Engagement Platform](https://mlsanity.com/paper/2102.07539/) (2021)
- [Robustness of Multi-Source MT to Transcription Errors](https://mlsanity.com/paper/2305.16894/) (2023)
- [How effective is Multi-source pivoting for Translation of Low Resource Indian Languages?](https://mlsanity.com/paper/2406.13332/) (2024)

## Abstract (arXiv)

Multi-source translation systems translate from multiple languages to a single target language. By using information from these multiple sources, these systems achieve large gains in accuracy. To train these systems, it is necessary to have corpora with parallel text in multiple sources and the target language. However, these corpora are rarely complete in practice due to the difficulty of providing human translations in all of the relevant languages. In this paper, we propose a data augmentation approach to fill such incomplete parts using multi-source neural machine translation (NMT). In our experiments, results varied over different language combinations but significant gains were observed when using a source language similar to the target language.