From Multiple-Choice to Extractive QA: A Case Study for English and Arabic
Lynn, Teresa Altakrori, Malik H. Magdy, Samar M. Das, Rocktim Jyoti Lyu, Chenyang Nasr, Mohamed Samih, Younes Chirkunov, Kirill Aji, Alham Fikri Nakov, Preslav
Lynn, Teresa
Altakrori, Malik H.
Magdy, Samar M.
Das, Rocktim Jyoti
Lyu, Chenyang
Nasr, Mohamed
Samih, Younes
Chirkunov, Kirill
Aji, Alham Fikri
Nakov, Preslav
Supervisor
Department
Natural Language Processing
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Type
Conference proceeding
Date
2025
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Language
English
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Abstract
The rapid evolution of Natural Language Processing (NLP) has favoured major languages such as English, leaving a significant gap for many others due to limited resources. This is especially evident in the context of data annotation, a task whose importance cannot be underestimated, but which is time-consuming and costly. Thus, any dataset for resource-poor languages is precious, in particular when it is task-specific. Here, we explore the feasibility of repurposing an existing multilingual dataset for a new NLP task: we repurpose a subset of the BELEBELE dataset (Bandarkar et al., 2023), which was designed for multiple-choice question answering (MCQA), to enable the more practical task of extractive QA (EQA) in the style of machine reading comprehension. We present annotation guidelines and a parallel EQA dataset for English and Modern Standard Arabic (MSA). We also present QA evaluation results for several monolingual and cross-lingual QA pairs including English, MSA, and five Arabic dialects. We aim to help others adapt our approach for the remaining 120 BELEBELE language variants, many of which are deemed under-resourced. We also provide a thorough analysis and share insights to deepen understanding of the challenges and opportunities in NLP task reformulation. © 2025 Association for Computational Linguistics.
Citation
T. Lynn et al., “From Multiple-Choice to Extractive QA: A Case Study for English and Arabic,” Proceedings - International Conference on Computational Linguistics, COLING, vol. Part, pp. 2456–2477, Jan. 2025.
Source
Proceedings - International Conference on Computational Linguistics, COLING
Conference
Keywords
Multiple-choice question answering (MCQA), Extractive question answering (EQA), Dataset repurposing, English-Arabic parallel dataset, Dialectal Arabic
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Association for Computational Linguistics