# Facebook AI's WAT19 Myanmar-English Translation Task Submission

arXiv: https://arxiv.org/abs/1910.06848 · HTML: https://mlsanity.com/paper/1910.06848/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2019.

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

- [Facebook AI WMT21 News Translation Task Submission](https://mlsanity.com/paper/2108.03265/) (2021)
- [MTet: Multi-domain Translation for English and Vietnamese](https://mlsanity.com/paper/2210.05610/) (2022)
- [AUGVIC: Exploiting BiText Vicinity for Low-Resource NMT](https://mlsanity.com/paper/2106.05141/) (2021)
- [Enriching Biomedical Knowledge for Low-resource Language Through Large-Scale Translation](https://mlsanity.com/paper/2210.05598/) (2022)

## Abstract (arXiv)

This paper describes Facebook AI's submission to the WAT 2019 Myanmar-English translation task. Our baseline systems are BPE-based transformer models. We explore methods to leverage monolingual data to improve generalization, including self-training, back-translation and their combination. We further improve results by using noisy channel re-ranking and ensembling. We demonstrate that these techniques can significantly improve not only a system trained with additional monolingual data, but even the baseline system trained exclusively on the provided small parallel dataset. Our system ranks first in both directions according to human evaluation and BLEU, with a gain of over 8 BLEU points above the second best system.