# From Brazilian Portuguese to European Portuguese

arXiv: https://arxiv.org/abs/2408.07457 · HTML: https://mlsanity.com/paper/2408.07457/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 14 Aug 2024.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation](https://mlsanity.com/paper/2302.09210/) (2023)
- [A Neural Approach to Language Variety Translation](https://mlsanity.com/paper/1807.00651/) (2018)
- [Carolina: a General Corpus of Contemporary Brazilian Portuguese with Provenance, Typology and Versioning Information](https://mlsanity.com/paper/2303.16098/) (2023)
- [Improving Similar Language Translation With Transfer Learning](https://mlsanity.com/paper/2108.03533/) (2021)

## Abstract (arXiv)

Brazilian Portuguese and European Portuguese are two varieties of the same language and, despite their close similarities, they exhibit several differences. However, there is a significant disproportion in the availability of resources between the two variants, with Brazilian Portuguese having more abundant resources. This inequity can impact the quality of translation services accessible to European Portuguese speakers. To address this issue, we propose the development of a Brazilian Portuguese to European Portuguese translation system, leveraging recent advancements in neural architectures and models. To evaluate the performance of such systems, we manually curated a gold test set comprising 500 sentences across five different topics. Each sentence in the gold test set has two distinct references, facilitating a straightforward evaluation of future translation models. We experimented with various models by fine-tuning existing Large Language Models using parallel data extracted from movie subtitles and TED Talks transcripts in both Brazilian and European Portuguese. Our evaluation involved the use of conventional automatic metrics as well as a human evaluation. In addition, all models were compared against ChatGPT 3.5 Turbo, which currently yields the best results.