From Multiple-Choice to Extractive QA: A Case Study for English and Arabic

• URL: http://arxiv.org/abs/2404.17342v2
• Date: Fri, 24 Jan 2025 05:18:29 GMT
• Title: From Multiple-Choice to Extractive QA: A Case Study for English and Arabic
• Authors: Teresa Lynn, Malik H. Altakrori, Samar Mohamed Magdy, Rocktim Jyoti Das, Chenyang Lyu, Mohamed Nasr, Younes Samih, Kirill Chirkunov, Alham Fikri Aji, Preslav Nakov, Shantanu Godbole, Salim Roukos, Radu Florian, Nizar Habash,
• Abstract summary: We explore the feasibility of repurposing an existing multilingual dataset for a new NLP task. We present annotation guidelines and a parallel EQA dataset for English and Modern Standard Arabic. We aim to help others adapt our approach for the remaining 120 BELEBELE language variants, many of which are deemed under-resourced.
• Score: 51.13706104333848
• License:
• 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.

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