UQA: Corpus for Urdu Question Answering

Samee Arif, Sualeha Farid, Awais Athar, Agha Ali Raza

Abstract
This paper introduces UQA, a novel dataset for question answering and text comprehension in Urdu, a low-resource language with over 70 million native speakers. UQA is generated by translating the Stanford Question Answering Dataset (SQuAD2.0), a large-scale English QA dataset, using a technique called EATS (Enclose to Anchor, Translate, Seek), which preserves the answer spans in the translated context paragraphs. The paper describes the process of selecting and evaluating the best translation model among two candidates: Google Translator and Seamless M4T. The paper also benchmarks several state-of-the-art multilingual QA models on UQA, including mBERT, XLM-RoBERTa, and mT5, and reports promising results. For XLM-RoBERTa-XL, we have an F1 score of 85.99 and 74.56 EM. UQA is a valuable resource for developing and testing multilingual NLP systems for Urdu and for enhancing the cross-lingual transferability of existing models. Further, the paper demonstrates the effectiveness of EATS for creating high-quality datasets for other languages and domains. The UQA dataset and the code are publicly available at www.github.com/sameearif/UQA
Anthology ID:
2024.lrec-main.1497
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
17237–17244
Language:
URL:
https://aclanthology.org/2024.lrec-main.1497
DOI:
Bibkey:
Cite (ACL):
Samee Arif, Sualeha Farid, Awais Athar, and Agha Ali Raza. 2024. UQA: Corpus for Urdu Question Answering. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 17237–17244, Torino, Italia. ELRA and ICCL.
Cite (Informal):
UQA: Corpus for Urdu Question Answering (Arif et al., LREC-COLING 2024)
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https://preview.aclanthology.org/proper-vol2-ingestion/2024.lrec-main.1497.pdf
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