TY - CHAP

T1 - Low Resource Question Answering

T2 - 5th Workshop on Resources for African Indigenous Languages, RAIL 2024

AU - Taffa, Tilahun Abedissa

AU - Assabie, Yaregal

AU - Usbeck, Ricardo

N1 - Publisher Copyright: © 2024 ELRA Language Resource Association.

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

KW - Amh-QuAD

KW - Amharic Question Answering Dataset

KW - Amharic Reading Comprehension

KW - Low Resource Question Answering

KW - Informatics

UR - http://www.scopus.com/inward/record.url?scp=85195211713&partnerID=8YFLogxK

M3 - Article in conference proceedings

AN - SCOPUS:85195211713

T3 - 5th Workshop on Resources for African Indigenous Languages, RAIL 2024 at LREC-COLING 2024 - Workshop Proceedings

SP - 124

EP - 132

BT - 5th Workshop on Resources for African Indigenous Languages, RAIL 2024 at LREC-COLING 2024 - Workshop Proceedings

A2 - Mabuya, Rooweither

A2 - Matfunjwa, Muzi

A2 - Setaka, Mmasibidi

A2 - van Zaanen, Menno

PB - European Language Resources Association (ELRA)

Y2 - 25 May 2024

ER -