# Lite Training Strategies for Portuguese-English and English-Portuguese Translation

arXiv: https://arxiv.org/abs/2008.08769 · HTML: https://mlsanity.com/paper/2008.08769/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [LLMEffiChecker: Understanding and Testing Efficiency Degradation of Large Language Models](https://mlsanity.com/paper/2210.03696/) (2022)
- [A cost-benefit analysis of cross-lingual transfer methods](https://mlsanity.com/paper/2105.06813/) (2021)
- [Towards Transparent AI: A Survey on Explainable Large Language Models](https://mlsanity.com/paper/2506.21812/) (2025)
- [Towards Transparent AI: A Survey on Explainable Language Models](https://mlsanity.com/paper/2509.21631/) (2025)

## Abstract (arXiv)

Despite the widespread adoption of deep learning for machine translation, it is still expensive to develop high-quality translation models. In this work, we investigate the use of pre-trained models, such as T5 for Portuguese-English and English-Portuguese translation tasks using low-cost hardware. We explore the use of Portuguese and English pre-trained language models and propose an adaptation of the English tokenizer to represent Portuguese characters, such as diaeresis, acute and grave accents. We compare our models to the Google Translate API and MarianMT on a subset of the ParaCrawl dataset, as well as to the winning submission to the WMT19 Biomedical Translation Shared Task. We also describe our submission to the WMT20 Biomedical Translation Shared Task. Our results show that our models have a competitive performance to state-of-the-art models while being trained on modest hardware (a single 8GB gaming GPU for nine days). Our data, models and code are available at https://github.com/unicamp-dl/Lite-T5-Translation.