AmQA: Amharic Question Answering Dataset
This addresses the problem of limited NLP resources for Amharic speakers and researchers, though it is incremental as it adapts existing methods to a new language.
The authors tackled the lack of a publicly available question answering dataset for Amharic by creating AmQA, the first such dataset, which includes 2628 question-answer pairs from 378 Wikipedia articles, with a baseline model achieving F-scores of 69.58 and 71.74 in different settings.
Question Answering (QA) returns concise answers or answer lists from natural language text given a context document. Many resources go into curating QA datasets to advance robust models' development. There is a surge of QA datasets for languages like English, however, this is not true for Amharic. Amharic, the official language of Ethiopia, is the second most spoken Semitic language in the world. There is no published or publicly available Amharic QA dataset. Hence, to foster the research in Amharic QA, we present the first Amharic QA (AmQA) dataset. We crowdsourced 2628 question-answer pairs over 378 Wikipedia articles. Additionally, we run an XLMR Large-based baseline model to spark open-domain QA research interest. The best-performing baseline achieves an F-score of 69.58 and 71.74 in reader-retriever QA and reading comprehension settings respectively.