# A Neural Approach to Language Variety Translation

arXiv: https://arxiv.org/abs/1807.00651 · HTML: https://mlsanity.com/paper/1807.00651/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2018.

## Built on by (most cited first, 6 of 6)

- [FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation](https://mlsanity.com/paper/2210.00193/) (2022)
- [TADA: Task-Agnostic Dialect Adapters for English](https://mlsanity.com/paper/2305.16651/) (2023)
- [Extremely low-resource machine translation for closely related languages](https://mlsanity.com/paper/2105.13065/) (2021)
- [OSN-MDAD: Machine Translation Dataset for Arabic Multi-Dialectal Conversations on Online Social Media](https://mlsanity.com/paper/2309.12137/) (2023)
- [Dialectal and Low-Resource Machine Translation for Aromanian](https://mlsanity.com/paper/2410.17728/) (2024)
- [From Brazilian Portuguese to European Portuguese](https://mlsanity.com/paper/2408.07457/) (2024)

## Abstract (arXiv)

In this paper we present the first neural-based machine translation system trained to translate between standard national varieties of the same language. We take the pair Brazilian - European Portuguese as an example and compare the performance of this method to a phrase-based statistical machine translation system. We report a performance improvement of 0.9 BLEU points in translating from European to Brazilian Portuguese and 0.2 BLEU points when translating in the opposite direction. We also carried out a human evaluation experiment with native speakers of Brazilian Portuguese which indicates that humans prefer the output produced by the neural-based system in comparison to the statistical system.