Can a Multichoice Dataset be Repurposed for Extractive Question Answering?
<|reference_start|>Can a Multichoice Dataset be Repurposed for Extractive Question Answering?: The rapid evolution of Natural Language Processing (NLP) has favored 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 existing datasets for a new NLP task: we repurposed the Belebele dataset (Bandarkar et al., 2023), which was designed for multiple-choice question answering (MCQA), to enable 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. Our aim is to enable others to adapt our approach for the 120+ other language variants in Belebele, many of which are deemed under-resourced. We also conduct a thorough analysis and share our insights from the process, which we hope will contribute to a deeper understanding of the challenges and the opportunities associated with task reformulation in NLP research.<|reference_end|>
@article{lynn2024can,
    title={Can a Multichoice Dataset be Repurposed for Extractive Question
  Answering?},
    author={Teresa Lynn, Malik H. Altakrori, Samar Mohamed Magdy, Rocktim Jyoti
  Das, Chenyang Lyu, Mohamed Nasr, Younes Samih, Alham Fikri Aji, Preslav
  Nakov, Shantanu Godbole, Salim Roukos, Radu Florian, Nizar Habash},
    journal={arXiv preprint arXiv:2404.17342},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17342},
    primaryClass={cs.CL cs.AI}
}