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        <title>Low Resource Question Answering: An Amharic Benchmarking Dataset</title>
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        <namePart type="given">Tilahun</namePart>
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            <title>Proceedings of the Fifth Workshop on Resources for African Indigenous Languages @ LREC-COLING 2024</title>
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            <namePart type="family">Mabuya</namePart>
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            <namePart type="given">Muzi</namePart>
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    <abstract>Question Answering (QA) systems return concise answers or answer lists based on natural language text, which uses a given context document. Many resources go into curating QA datasets to advance the development of robust QA models. There is a surge in QA datasets for languages such as English; this is different for low-resource languages like Amharic. Indeed, there is no published or publicly available Amharic QA dataset. Hence, to foster further research in low-resource QA, we present the first publicly available benchmarking Amharic Question Answering Dataset (Amh-QuAD). We crowdsource 2,628 question-answer pairs from over 378 Amharic Wikipedia articles. Using the training set, we fine-tune an XLM-R-based language model and introduce a new reader model. Leveraging our newly fine-tuned reader run a baseline model to spark open-domain Amharic QA research interest. The best- performing baseline QA achieves an F-score of 80.3 and 81.34 in retriever-reader and reading comprehension settings.</abstract>
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        <url>https://aclanthology.org/2024.rail-1.14</url>
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%0 Conference Proceedings
%T Low Resource Question Answering: An Amharic Benchmarking Dataset
%A Taffa, Tilahun Abedissa
%A Usbeck, Ricardo
%A Assabie, Yaregal
%Y Mabuya, Rooweither
%Y Matfunjwa, Muzi
%Y Setaka, Mmasibidi
%Y van Zaanen, Menno
%S Proceedings of the Fifth Workshop on Resources for African Indigenous Languages @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F taffa-etal-2024-low
%X Question Answering (QA) systems return concise answers or answer lists based on natural language text, which uses a given context document. Many resources go into curating QA datasets to advance the development of robust QA models. There is a surge in QA datasets for languages such as English; this is different for low-resource languages like Amharic. Indeed, there is no published or publicly available Amharic QA dataset. Hence, to foster further research in low-resource QA, we present the first publicly available benchmarking Amharic Question Answering Dataset (Amh-QuAD). We crowdsource 2,628 question-answer pairs from over 378 Amharic Wikipedia articles. Using the training set, we fine-tune an XLM-R-based language model and introduce a new reader model. Leveraging our newly fine-tuned reader run a baseline model to spark open-domain Amharic QA research interest. The best- performing baseline QA achieves an F-score of 80.3 and 81.34 in retriever-reader and reading comprehension settings.
%U https://aclanthology.org/2024.rail-1.14
%P 124-132

Markdown (Informal)

[Low Resource Question Answering: An Amharic Benchmarking Dataset](https://aclanthology.org/2024.rail-1.14) (Taffa et al., RAIL-WS 2024)

• Low Resource Question Answering: An Amharic Benchmarking Dataset (Taffa et al., RAIL-WS 2024)

ACL

• Tilahun Abedissa Taffa, Ricardo Usbeck, and Yaregal Assabie. 2024. Low Resource Question Answering: An Amharic Benchmarking Dataset. In Proceedings of the Fifth Workshop on Resources for African Indigenous Languages @ LREC-COLING 2024, pages 124–132, Torino, Italia. ELRA and ICCL.