dsfsi/flores-fix-4-africa

# FLORES Dataset Corrections for Four African Languages This project focuses on correcting the FLORES evaluation dataset (dev and devtest) for four African languages: **Hausa**, **Northern Sotho (Sepedi)**, **Xitsonga**, and **isiZulu**. The original dataset, though groundbreaking in covering low-resource languages, contained several inconsistencies and inaccuracies in these languages, which could affect the quality of evaluations in Natural Language Processing (NLP) tasks, especially for machine translation. ## Overview In this project, native speakers meticulously reviewed and corrected the dataset to ensure improved accuracy and reliability for each language. Our goal was to enhance the integrity of downstream NLP tasks that use this data. ### What We Did: 1. **Reviewed and Corrected Errors**: Identified and implemented corrections to translation inconsistencies and inaccuracies in the dataset. 2. **Statistical Analysis**: Conducted statistical comparisons between the original and corrected datasets, highlighting the differences and improvements made. 3. **Improved Dataset Quality**: Enhanced linguistic accuracy and reliability, ensuring more effective evaluation of NLP tasks involving these languages. ### Key Corrections: - **Hausa**: The Hausa translations revealed numerous inconsistencies, suggesting a significant portion may have been automatically generated, particularly due to unclear or incoherent phrasing. Comparisons with the Hausa FLORES dataset and Google Translate showed that many lexical choices were incorrect and aligned with Google’s outputs, raising concerns about the quality of the original translations. Additional issues included improper translations of named entities and the frequent omission of special characters in standardized Hausa alphabets. - **Northern Sotho (Sepedi)**: The translations in Northern Sotho displayed a need for improvement in vocabulary consistency, syntax, and the accurate conveyance of technical terms. While most te …