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
- Cite (ACL):
- Long Doan, Linh The Nguyen, Nguyen Luong Tran, Thai Hoang, and Dat Quoc Nguyen. 2021. PhoMT: A High-Quality and Large-Scale Benchmark Dataset for Vietnamese-English Machine Translation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4495–4503, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- PhoMT: A High-Quality and Large-Scale Benchmark Dataset for Vietnamese-English Machine Translation (Doan et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2021.emnlp-main.369.pdf
- Code
- vinairesearch/phomt
- Data
- PhoMT, OpenSubtitles, WikiMatrix