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.
@article{arxiv.2110.12199,
title = {PhoMT: A High-Quality and Large-Scale Benchmark Dataset for Vietnamese-English Machine Translation},
author = {Long Doan and Linh The Nguyen and Nguyen Luong Tran and Thai Hoang and Dat Quoc Nguyen},
journal= {arXiv preprint arXiv:2110.12199},
year = {2021}
}
Comments
To appear in Proceedings of EMNLP 2021 (main conference). The first three authors contribute equally to this work