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
BibTeX
@inproceedings{doan-etal-2021-phomt,
title = "{P}ho{MT}: A High-Quality and Large-Scale Benchmark Dataset for {V}ietnamese-{E}nglish Machine Translation",
author = "Doan, Long and
Nguyen, Linh The and
Tran, Nguyen Luong and
Hoang, Thai and
Nguyen, Dat Quoc",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.369/",
doi = "10.18653/v1/2021.emnlp-main.369",
pages = "4495--4503"
}