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

Keywords: Vietnamese, English, Machine Translation, PhoMT

TL;DR: A High-Quality and Large-Scale Benchmark Dataset for Vietnamese-English Machine Translation; The first three authors contribute equally to this work; Accepted to EMNLP 2021 main conference

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.