We introduce MTet, the largest publicly available parallel corpus for
English-Vietnamese translation. MTet consists of 4.2M high-quality training
sentence pairs and a multi-domain test set refined by the Vietnamese research
community. Combining with previous works on English-Vietnamese translation, we
grow the existing parallel dataset to 6.2M sentence pairs. We also release the
first pretrained model EnViT5 for English and Vietnamese languages. Combining
both resources, our model significantly outperforms previous state-of-the-art
results by up to 2 points in translation BLEU score, while being 1.6 times
smaller.

MTet 是最大的公开平行语料库，包含 420 万高质量的训练句对和一个多领域测试集，同时发布了首个预训练的 EnViT5 模型，与之前的研究相比，其翻译 BLEU 得分最多提高 2 分，同时比之前的模型小 1.6 倍。

MTet: 英越多领域翻译

MTet: Multi-domain Translation for English and Vietnamese

Biomedical data and benchmarks are highly valuable yet very limited in
low-resource languages other than English such as Vietnamese. In this paper, we
make use of a state-of-the-art translation model in English-Vietnamese to
translate and produce both pretrained as well as supervised data in the
biomedical domains. Thanks to such large-scale translation, we introduce
ViPubmedT5, a pretrained Encoder-Decoder Transformer model trained on 20
million translated abstracts from the high-quality public PubMed corpus.
ViPubMedT5 demonstrates state-of-the-art results on two different biomedical
benchmarks in summarization and acronym disambiguation. Further, we release
ViMedNLI - a new NLP task in Vietnamese translated from MedNLI using the
recently public En-vi translation model and carefully refined by human experts,
with evaluations of existing methods against ViPubmedT5.

本研究使用英越翻译模型将生物医学数据和基准转化为越南语，并利用大规模的越南语翻译数据训练得到了 ViPubmedT5 编解码 Transformer 模型，该模型在医学摘要和首字母缩写消歧方面取得了最先进的成果，并发布了 ViMedNLI 数据集用于 NLP 任务。