Year: 2024
Type: conference-paper
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 bestperforming baseline QA achieves an F-score of 80.3 and 81.34 in retriever-reader and reading comprehension settings.
Institutions: Leuphana University of Lüneburg, Addis Ababa University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7131799150