P ho MT : A High-Quality and Large-Scale Benchmark Dataset for V ietnamese- E nglish Machine Translation (2021)

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

## introduction vietnam has achieved rapid economic growth in the last two decades @xcite . it is now an attractive destination for trade and investment. due to the language barrier, foreigners usually rely on automatic machine translation (mt) systems to translate vietnamese texts into their native language or another language they are familiar with, e.g. the global language english, so they could quickly catch up with ongoing events in vietnam. thus the demand for high-quality vietnamese-english mt has rapidly increased. however, state-of-theart mt models require high-quality and large-scale corpora for training to be able to reach near humanlevel translation quality @xcite . despite being one of the most spoken languages in the world with about 100m speakers, vietnamese is referred to as a low-resource language in mt research because publicly available parallel corpora for vietnamese in general and in particular for vietnamese-english mt are not large enough or have low-quality translation pairs, including those with different sentence meaning (i.e. misalignment). two main concerns are detailed as follows: as the first contribution, to help handle the two concerns above, we present a high-quality and large-scale vietnamese-english parallel dataset, named phomt, that consists of 3.02m sentence pairs. here, from phomt, we also prepare 38k sentence pairs with manually qualitative inspection, that are used for validation and test. we believe that our dataset construction process will help develop more efficient data creation strategies for other low-resource languages. as the second contribution, we empirically investigate strong baselines on our dataset, including transformer-base, transformer-big @xcite and the pre-trained sequence-to-sequence denoising autoencoder mbart @xcite , and compare these baselines with well-known automatic translation engines. we find that mbart obtains the highest scores in terms of both automatic and human evaluations on both translation directions. to the best of our knowledge, this is the first largescale empirical study for vietnamese-english mt. as our final contribution, we publicly release our phomt dataset for research or educational purposes. we hope phomt together with our empirical study can serve as a starting point for future vietnamese-english mt research and applications. ## our phomt dataset our dataset construction process consists of 4 phases. the 1st phase is to collect parallel document pairs. the 2nd phase is a pre-processing step that is to produce cleaned and high-quality parallel document pairs and then extract sentences from these pairs. the 3rd phase is to align parallel sentences within a pair of parallel documents. the 4th phase is a post-processing step that is to filter out duplicated parallel sentence pairs and manually verify the quality of validation and test sets. ## conclusion we have presented phomt-a high-quality and large-scale vietnamese-english parallel dataset of 3.02m sentence pairs. we empirically conduct experiments on our phomt dataset to compare strong baselines and demonstrate the effectiveness of the pre-trained denoising auto-encoder mbart for neural mt in both automatic and human evaluations. we hope that the public release of our dataset can serve as the starting point for further vietnamese-english mt research and applications.