LIACC/Emakhuwa-Portuguese-News-MT

News Parallel Dataset for Emakhuwa of Mozambique

This repository contains releases of parallel data for machine translation in Mozambican languages. Currently, it supports one language pair, Portuguese-Emakhuwa, Emakhuwa being the widely spoken language in Mozambique.

Dataset Details

Dataset Description

• Funded by: This dataset was created with support from Lacuna Fund, the world’s first collaborative effort to provide data scientists, researchers, and social entrepreneurs in low- and middle-income contexts globally with the resources they need to produce labeled datasets that address urgent problems in their communities. Lacuna Fund is a funder collaborative that includes The Rockefeller Foundation, Google.org, Canada’s International Development Research Centre, the German Federal Ministry for Economic Cooperation and Development (BMZ) with GIZ as implementing agency, Wellcome Trust, Gordon and Betty Moore Foundation, Patrick J. McGovern Foundation, and The Robert Wood Johnson Foundation. See https://lacunafund.org/about/ for more information.
• Language(s) (NLP): Emakhuwa (vmw), Portuguese (pt)
• License: CC BY 4.0

BibTeX:

The dataset paper was published in EMNLP 2024.

Please cite as:

@inproceedings{ali-etal-2024-building,
    title = "Building Resources for Emakhuwa: Machine Translation and News Classification Benchmarks",
    author = "Ali, Felermino D. M. A.  and
      Lopes Cardoso, Henrique  and
      Sousa-Silva, Rui",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.emnlp-main.824",
    pages = "14842--14857",
    abstract = "This paper introduces a comprehensive collection of NLP resources for Emakhuwa, Mozambique{'}s most widely spoken language. The resources include the first manually translated news bitext corpus between Portuguese and Emakhuwa, news topic classification datasets, and monolingual data. We detail the process and challenges of acquiring this data and present benchmark results for machine translation and news topic classification tasks. Our evaluation examines the impact of different data types{---}originally clean text, post-corrected OCR, and back-translated data{---}and the effects of fine-tuning from pre-trained models, including those focused on African languages.Our benchmarks demonstrate good performance in news topic classification and promising results in machine translation. We fine-tuned multilingual encoder-decoder models using real and synthetic data and evaluated them on our test set and the FLORES evaluation sets. The results highlight the importance of incorporating more data and potential for future improvements.All models, code, and datasets are available in the \url{https://huggingface.co/LIACC} repository under the CC BY 4.0 license.",
}

Dataset Structure:

• Training: 16,574 sentences
• Testing: 993 sentences
• Development/Validation: 964 sentences

Translation:

The translation was done by ten expert translators.

Considerations for Using the Data:

Considerations for Using the Data:

Data exclusively for the Emakhuwa-central variant (i.e. ISO code vmw)

Dataset Card Contact

Felermino Ali