Tigrinya-SQuAD: Machine-Translated Training Dataset

Tigrinya-SQuAD is a machine-translated and filtered version of the English SQuAD 1.1 training dataset, automatically converted to Tigrinya for training question-answering models in low-resource settings.

This silver dataset serves as training data for Tigrinya question-answering systems. For evaluation and benchmarking, please use the gold-standard TiQuAD dataset, which contains human-annotated validation and test sets.

Published with the paper: Question-Answering in a Low-resourced Language: Benchmark Dataset and Models for Tigrinya (ACL 2023)

Related repositories:

• TiQuAD (gold dataset)
• The paper's GitHub repository

Dataset Overview

Tigrinya-SQuAD is designed as training data for extractive question answering in Tigrinya, a low-resource Semitic language primarily spoken in Eritrea and Ethiopia. The dataset features:

• Source data: English SQuAD 1.1 training part, which is based on Wikipedia articles
• Machine-translated: Automatically translated from English SQuAD 1.1 using neural machine translation
• Filtered: Post-processed with heuristic filtering to improve quality and discarded low-quality samples
• Training-only: Contains only training split; use TiQuAD for validation/testing
• SQuAD format: Maintains compatibility with standard QA frameworks
• Not human verified, to be used for training but not for final evaluation

Split	Articles	Paragraphs	Questions	Answers
Train	442	17,391	46,737	46,737

How to Load Tigrinya-SQuAD

from datasets import load_dataset

# Load the dataset
tigrinya_squad = load_dataset("fgaim/tigrinya-squad", trust_remote_code=True)
print(tigrinya_squad)

DatasetDict({
    train: Dataset({
        features: ['id', 'title', 'context', 'question', 'answers'],
        num_rows: 46737
    })
})

Data Fields

• id: Unique identifier for each question-answer pair
• title: Title of the source article (translated from English)
• context: The paragraph containing the answer (in Tigrinya)
• question: The question to be answered (in Tigrinya)
• answers: Dictionary containing:
  • text: List with single answer string (training data has one answer per question)
  • answer_start: List with position where answer begins in the context

Evaluation and Benchmarking

This dataset contains only training data, for proper evaluation of Tigrinya question-answering models use TiQuAD, which provides multireference, human-annotated validation/test splits. Both datasets can be combined during training for best results as reported in the paper.

Citation

If you use this dataset in your work, please cite the original TiQuAD paper:

@inproceedings{gaim-etal-2023-tiquad,
    title = "Question-Answering in a Low-resourced Language: Benchmark Dataset and Models for {T}igrinya",
    author = "Fitsum Gaim and Wonsuk Yang and Hancheol Park and Jong C. Park",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.661",
    pages = "11857--11870",
}

Data Quality and Limitations

As a machine-translated dataset, Tigrinya-SQuAD has inherent limitations:

• Translation errors: Some questions/answers may have translation artifacts
• Cultural adaptation: Context may not perfectly align with Tigrinya cultural references
• Not suitable for model evaluation or human performance comparison but for training purpose only.

If you identify any issues with the dataset, please contact us at [email protected].

Acknowledgments

This dataset builds upon the foundational work of the Stanford Question Answering Dataset (SQuAD) and the human-annotated TiQuAD dataset. We thank the original SQuAD creators for making their data freely available.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.