# Tencent AI Lab - Shanghai Jiao Tong University Low-Resource Translation System for the WMT22 Translation Task

arXiv: https://arxiv.org/abs/2210.08742 · HTML: https://mlsanity.com/paper/2210.08742/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 17 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [Unsupervised Machine Translation Using Monolingual Corpora Only](https://mlsanity.com/paper/1711.00043/) (2017)
- [Massively Multilingual Neural Machine Translation](https://mlsanity.com/paper/1903.00089/) (2019)
- [Building Machine Translation Systems for the Next Thousand Languages](https://mlsanity.com/paper/2205.03983/) (2022)
- [Towards Continual Learning for Multilingual Machine Translation via Vocabulary Substitution](https://mlsanity.com/paper/2103.06799/) (2021)
- [Data Rejuvenation: Exploiting Inactive Training Examples for Neural Machine Translation](https://mlsanity.com/paper/2010.02552/) (2020)
- [Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation](https://mlsanity.com/paper/2106.00941/) (2021)
- [Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation](https://mlsanity.com/paper/2203.08394/) (2022)

## Built on by (most cited first, 8 of 8)

- [Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine](https://mlsanity.com/paper/2301.08745/) (2023)
- [Exploring Human-Like Translation Strategy with Large Language Models](https://mlsanity.com/paper/2305.04118/) (2023)
- [Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model](https://mlsanity.com/paper/2401.12873/) (2024)
- [A Paradigm Shift: The Future of Machine Translation Lies with Large Language Models](https://mlsanity.com/paper/2305.01181/) (2023)
- [Machine Translation with Large Language Models: Prompt Engineering for Persian, English, and Russian Directions](https://mlsanity.com/paper/2401.08429/) (2024)
- [Unsupervised Sign Language Translation and Generation](https://mlsanity.com/paper/2402.07726/) (2024)
- [Gradable ChatGPT Translation Evaluation](https://mlsanity.com/paper/2401.09984/) (2024)
- [A Single Model Ensemble Framework for Neural Machine Translation using Pivot Translation](https://mlsanity.com/paper/2502.01182/) (2025)

## Abstract (arXiv)

This paper describes Tencent AI Lab - Shanghai Jiao Tong University (TAL-SJTU) Low-Resource Translation systems for the WMT22 shared task. We participate in the general translation task on EnglishLeftrightarrowLivonian. Our system is based on M2M100 with novel techniques that adapt it to the target language pair. (1) Cross-model word embedding alignment: inspired by cross-lingual word embedding alignment, we successfully transfer a pre-trained word embedding to M2M100, enabling it to support Livonian. (2) Gradual adaptation strategy: we exploit Estonian and Latvian as auxiliary languages for many-to-many translation training and then adapt to English-Livonian. (3) Data augmentation: to enlarge the parallel data for English-Livonian, we construct pseudo-parallel data with Estonian and Latvian as pivot languages. (4) Fine-tuning: to make the most of all available data, we fine-tune the model with the validation set and online back-translation, further boosting the performance. In model evaluation: (1) We find that previous work underestimated the translation performance of Livonian due to inconsistent Unicode normalization, which may cause a discrepancy of up to 14.9 BLEU score. (2) In addition to the standard validation set, we also employ round-trip BLEU to evaluate the models, which we find more appropriate for this task. Finally, our unconstrained system achieves BLEU scores of 17.0 and 30.4 for English to/from Livonian.