PhoMT: A High-Quality and Large-Scale Benchmark Dataset for Vietnamese-English Machine Translation

Long Doan, Linh The Nguyen, Nguyen Luong Tran, Thai Hoang, Dat Quoc Nguyen


Abstract
We introduce a high-quality and large-scale Vietnamese-English parallel dataset of 3.02M sentence pairs, which is 2.9M pairs larger than the benchmark Vietnamese-English machine translation corpus IWSLT15. We conduct experiments comparing strong neural baselines and well-known automatic translation engines on our dataset and find that in both automatic and human evaluations: the best performance is obtained by fine-tuning the pre-trained sequence-to-sequence denoising auto-encoder mBART. To our best knowledge, this is the first large-scale Vietnamese-English machine translation study. We hope our publicly available dataset and study can serve as a starting point for future research and applications on Vietnamese-English machine translation. We release our dataset at: https://github.com/VinAIResearch/PhoMT
Anthology ID:
2021.emnlp-main.369
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4495–4503
Language:
URL:
https://aclanthology.org/2021.emnlp-main.369
DOI:
10.18653/v1/2021.emnlp-main.369
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.369.pdf
Code
 vinairesearch/phomt
Data
OpenSubtitlesWikiMatrix