Machine translation (MT), the task of mapping content from one language to another automatically by computers, is unarguably one of the most important applications of natural language processing (NLP). Despite great progress in MT using AI, specifically deep learning and neural networks, English-Vietnamese translation quality still lags behind, mostly due to the lack of high-quality datasets at scale. Recently, VietAI1VietAI is a non-profit organization with the mission of building a community of world-class AI experts in Vietnam. VietAI has nurtured and trained thousands of students and experts in AI, 3 of whom are the first Google Developer Experts in Machine Learning in Vietnam. has released a high-quality English-Vietnamese translation corpus of 3.3M examples to the research community2There are also concurrent efforts from VinAI and VLSP. to spur further progress in research.


Continuing this effort, we are excited to introduce our second release of VietAI’s MTet project, which stands for Multi-domain Translation for English and VieTnamese. With this release, we further improved on the first-ever multi-domain English-Vietnamese translation dataset at scale to release up to 4.2M examples across 11 domains. In addition, we demonstrated state-of-the-art results on IWSLT’15 (+3.5 BLEU for English-Vietnamese). We hope that our efforts will inspire further contributions to an ever-growing repository of high-quality datasets for the Vietnamese NLP community.


We report a seven-year-old female presenting with fever, dry cough, and abdominal pain after that.
This Law was passed on June 9, 2000 by the Xth National Assembly of the Socialist Republic of Vietnam at its 7th session.
The problem comes when grasping and attachment dominate your mind and you put all your faith in other people and material possessions.
Without arguments, 'print' displays the entire partition table. However with the following arguments it performs various other actions.
Then we could buy a castle and live in it with armed guards patrolling outside to keep out Count Olaf and his troupe.
Many great artists such as Michelangelo, da Vinci and Sir Edwin Henry Landseer were ambidextrous.
Table 1. Some examples in different domains from the training set.


In this release, we cleaned and deduplicated our first version of the dataset (datav1), while adding in 1.2M training text pairs from various text sources. This grows our dataset from 3.0M to near 4.2M training text pairs in datav2.

The additional data comes from two sources. First, we use modelv1 to score, filter, and pair high-quality data from existing large and noisy sources (OpenSubtitles, MultiCCAligned, and Wikilingua) that has yet to be incorporated in datav1. Second, we perform a mix of automated and manual scraping from a list of 30 public websites, ranging across multiple different domains such as medical publications, religious texts, engineering articles, literature, news, and poems. We also calibrated our previous test set to balance between different translation domains.


Utilizing Google Cloud Platform, TPUs, and Tensorflow

By using TPU v3-8 and TPU v3-32, we were able to train larger Transformer models and achieved state-of-the-art results on multiple test sets. Utilizing Google Cloud Storage with flexibility in region, we are also able to distribute our data pipelines for training on each specific Transformer model and TPUs (v2-8, v3-8, and v3-32). The improvement in the training speed of TPU v3 (compared to TPU v2-8) also allows us to train large models for longer and faster.

Thanks to GCP credit generously provided by Google Cloud, we are able to run 10 TPUv2 in parallel to score and filter high-quality training data from very large but noisy translation datasets such as MultiCC (20M sentences) and OpenSubtitles corpus (3.5M sentences), in the end, contributed close to half of the new data.

In our experiments, we made use of the tensor2tensor library, written in Tensorflow, which supports training high-performing translation models on TPUs.

Model and Results

In addition to improving dataset size and diversity, we employed vanilla Transformer architecture with Transformer-tall18 setting, doubled in size when compared to Transformer-tall9 (See Table 1 for more details) and better in translation quality in IWSLT2015 benchmark.

With the help of Google’s Cloud TPUv3 and Google Cloud Platform infrastructure, we are able to train large models for faster and longer, in the end achieving state-of-the-art translation quality on our Transformer models v2.


Model #Params #Layers #Heads Hidden size Intermediate size
Transformer-tall9 (ours) 180M 9 12 768 3072
Transformer-tall18 (MTet) 380M 18 12 768 3072
mBART 610M 12 16 1024 4096
M2M100 1.2B 24 16 1024 8192
Table 2. Hyperparameter settings

In Figure 1, we report the results of MTet models on the IWSLT2015 Translation corpus. MTet Transformer-tall18 models achieve state-of-the-art translation for both Vietnamese and English. There is also a significant improvement from training on a larger MTet dataset, contributing to 0.7% and 1.6% for En-Vi and Vi-En respectively.

Our Transformer-tall18 model, training on our new release high-quality VietAI Translation dataset, outperforms the existing M2M100, a larger model trained on a much larger, but noiser dataset (Figure 2). We achieve state-of-the-art results on both En-Vi and Vi-En tasks (5.8% and 12.4% higher than M2M100 respectively) while being much smaller.

Figure 1. Improvements made from our first release to this second release on on IWSLT15 test set.
Figure 2. Comparison between pre-trained translation models and our purely supervised MTet.

Below, we show the ability of our model to translate quality outputs across different domains.

    Input: The erosion of mosses on the surface of Hue imperial citadel has caused a lot of harm and is currently an urgent problem to be solved.
    Groundtruth: Sự xâm thực của các loài rêu trên bề mặt quần thể di tích Huế đã gây ra nhiều tác hại và hiện đang là một vấn đề hết sức cấp bách cần giải quyết
    M2M100: Sự xói mòn của đầm lầy trên bề mặt pháo đài hoàng gia Hue đã gây ra rất nhiều thiệt hại và hiện nay là một vấn đề cấp bách cần giải quyết.
    Ours (MTet): Sự xói mòn của rêu trên bề mặt của thành lũy Hoàng gia Huế đã gây ra rất nhiều thiệt hại và hiện là một vấn đề cấp bách cần được giải quyết .
    Input: Authors-cum-owners of architectural works shall have the non-transferable personal rights
    Groundtruth: Tác giả đồng thời là chủ sở hữu tác phẩm kiến trúc có các quyền nhân thân không được chuyển giao cho người khác
    M2M100: Tác giả cùng chủ sở hữu của các tác phẩm kiến trúc sẽ có các quyền cá nhân không thể chuyển nhượng
    Ours (MTet): Tác giả cùng chủ sở hữu của các tác phẩm kiến trúc sẽ có các quyền cá nhân không thể chuyển nhượng
    Input: I neglected to mention it in my letter, but I was a whaler once.
    Groundtruth: Tôi chần chừ không tiết lộ trong lá thư rằng tôi từng là người đánh cá voi
    M2M100: Tôi đã bỏ qua để đề cập đến nó trong thư của tôi, nhưng tôi đã từng là một con cá voi.
    Ours (MTet): Tôi đã quên đề cập đến nó trong lá thư của tôi , nhưng tôi đã từng là một thợ săn cá voi
Table 3. Some good translation outputs from different models across different domains

Besides that, our model however couldn't perform well in some examples below.

    Input: Dị vật đường ăn là một cấp cứu trong lĩnh vực Tai Mũi Họng, kiến thức của quần chúng nhân dân về dị vật đường ăn còn một số hạn chế.
    Groundtruth: Foreign body ingestion is an emergency in otology, knowledge of people about foreign bodies ingestion is not enough.
    M2M100: Sugar minerals are an emergency in the field of Nose throat, the knowledge of the people's masses of food minerals is some limited.
    Ours (MTet): Meal - sugar herniation is an emergency in ENT, knowledge of people about food - sugar herniation is limited.
    Input: Không được sử dụng nguồn vốn sự nghiệp không có tính chất xây dựng cơ bản để đầu tư xây dựng mới, hoặc mở rộng.
    Groundtruth: It is forbidden to use the service capital without a capital construction character to invest in new constructions or in expansion.
    M2M100: Do not use funds of non-fundamental construction nature for new construction, or expansion investments.
    Ours (MTet): Non - business capital sources other than capital construction capital must not be used for investment in new construction , expansion .
    Input: Dân chúng tôi cũng sẽ được hưởng lợi như người của cô từ vụ mùa bội thu
    Groundtruth: Our people, as much as yours, will benefit from a good harvest.
    M2M100: Our people will benefit as well as yours from the harvest.
    Ours (MTet): We will also benefit like your men from the harvest.
Table 4. Some bad translation outputs from different models across different domains


Our detailed data statistics can be found here.

Refer to this work:

        author  = {Chinh Ngo, Hieu Tran, Long Phan, Trieu H. Trinh, Hieu Nguyen, Minh Nguyen, Minh-Thang Luong},
        title   = {MTet: Multi-domain Translation for English and Vietnamese},
        journal = {},
        year    = {2022},


Special thanks to Đào Lê Dũng, Nguyễn Trần Khang, Như Mai, Sáng Lê for their contributions to our web scraping efforts, and Phan Huy Hoàng, Vũ Trí Hậu for building this landing page.

We would like to thank the Google TPU Research Cloud (TRC) program, Soonson Kwon (Google ML Ecosystem programs Lead) and Ba Ngoc Nguyen (Google Developer Experts in ML) for their support. This project also receives generous support from, specifically Mr. Kim Cuong Pham, and, for managing and labeling our multi-domain test data. We appreciate the effort of volunteers who refine the multi-domain test dataset: Hàn Thọ Hoà, Trần Viết Đình, Dương Ngọc Doanh, Nguyễn Bùi Thiên Anh, Nguyễn Công Khanh, Triệu Khắc Đức, Trần Thị Lành, Hoàng An, Hữu Doanh, Ngọc Khánh, Trọng Văn, Huỳnh Anh, Thu Huyền.

Thanks to Hugging Face Spaces for the quick support in rolling out a demo version of our robust models to make it easier for people to play with.


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