# The VolcTrans System for WMT22 Multilingual Machine Translation Task

arXiv: https://arxiv.org/abs/2210.11599 · HTML: https://mlsanity.com/paper/2210.11599/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 27 references that are in the map.
- First posted on arXiv 20 Oct 2022.

## Builds on (most cited first, 17 of 17)

- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation](https://mlsanity.com/paper/2004.11867/) (2020)
- [Towards a Cleaner Document-Oriented Multilingual Crawled Corpus](https://mlsanity.com/paper/2201.06642/) (2022)
- [Contrastive Learning for Many-to-many Multilingual Neural Machine Translation](https://mlsanity.com/paper/2105.09501/) (2021)
- [A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation](https://mlsanity.com/paper/2205.02022/) (2022)
- [Learning Joint Multilingual Sentence Representations with Neural Machine Translation](https://mlsanity.com/paper/1704.04154/) (2017)
- [Multilingual Machine Translation Systems from Microsoft for WMT21 Shared Task](https://mlsanity.com/paper/2111.02086/) (2021)
- [Language Tags Matter for Zero-Shot Neural Machine Translation](https://mlsanity.com/paper/2106.07930/) (2021)
- [LightSeq2: Accelerated Training for Transformer-based Models on GPUs](https://mlsanity.com/paper/2110.05722/) (2021)
- [Transfer Learning in Multilingual Neural Machine Translation with Dynamic Vocabulary](https://mlsanity.com/paper/1811.01137/) (2018)
- [Adapting Multilingual Neural Machine Translation to Unseen Languages](https://mlsanity.com/paper/1910.13998/) (2019)
- [Multilingual Neural Machine Translation with Deep Encoder and Multiple Shallow Decoders](https://mlsanity.com/paper/2206.02079/) (2022)
- [Back-translation for Large-Scale Multilingual Machine Translation](https://mlsanity.com/paper/2109.08712/) (2021)
- [PARAGEN : A Parallel Generation Toolkit](https://mlsanity.com/paper/2210.03405/) (2022)

## Abstract (arXiv)

This report describes our VolcTrans system for the WMT22 shared task on large-scale multilingual machine translation. We participated in the unconstrained track which allows the use of external resources. Our system is a transformerbased multilingual model trained on data from multiple sources including the public training set from the data track, NLLB data provided by Meta AI, self-collected parallel corpora, and pseudo bitext from back-translation. A series of heuristic rules clean both bilingual and monolingual texts. On the official test set, our system achieves 17.3 BLEU, 21.9 spBLEU, and 41.9 chrF2++ on average over all language pairs. The average inference speed is 11.5 sentences per second using a single Nvidia Tesla V100 GPU. Our code and trained models are available at https://github.com/xian8/wmt22