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NaijaRC: A Multi-choice Reading Comprehension Dataset for Nigerian Languages

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arxiv 2308.09768 v3 pith:QH3SHKSA submitted 2023-08-18 cs.CL

Anuoluwapo Aremu , Jesujoba O. Alabi , Daud Abolade , Nkechinyere F. Aguobi , Shamsuddeen Hassan Muhammad , David Ifeoluwa Adelani This is my paper · ORCID
classification cs.CL
keywords comprehensiondatasetreadinglanguagesmulti-choicenaijarcresultsadditionally
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In this paper, we create NaijaRC: a new multi-choice Reading Comprehension dataset for three native Nigeria languages that is based on high-school reading comprehension examination. We provide baseline results by performing cross-lingual transfer using existing English RACE and Belebele training dataset based on a pre-trained encoder-only model. Additionally, we provide results by prompting large language models (LLMs) like GPT-4.

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  1. Y-NQ: English-Yor\`ub\'a Evaluation dataset for Open-Book Reading Comprehension and Text Generation

    cs.CL 2024-12 conditional novelty 6.0 of 10

    Y-NQ is a 358-question open-book reading comprehension benchmark for English and Yorùbá, and the paper reports that GPT-4o, o1-mini, and Llama-3.1-8b all perform worse on Yorùbá than on English.

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