Fine-tuning mBART with TF-IDF-selected back-translated monolingual sentences raises test BLEU from 38.22 to 38.97 for Vietnamese-to-Chinese and from 35.58 to 38.90 for Chinese-to-Vietnamese.
PhoMT: A High-Quality and Large-Scale Benchmark Dataset for Vietnamese-English Machine Translation
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
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.
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
An Efficient Approach for Machine Translation on Low-resource Languages: A Case Study in Vietnamese-Chinese
Fine-tuning mBART with TF-IDF-selected back-translated monolingual sentences raises test BLEU from 38.22 to 38.97 for Vietnamese-to-Chinese and from 35.58 to 38.90 for Chinese-to-Vietnamese.