# BanglaQuAD: A Bengali Open-domain Question Answering Dataset Source: [BanglaQuAD: A Bengali Open-domain Question Answering Dataset]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:41:00.437Z Authors: Md Rashad Al Hasan Rony, Sudipto Kumar Shaha, Rakib Al Hasan, Sumon Kanti Dey, Amzad Hossain Rafi, Amzad Hossain Rafi, Ashraf Hasan Sirajee, Jens Lehmann Published: 2024-10-14T07:39:59.000Z ## Abstract Bengali is the seventh most spoken language on earth, yet considered a low-resource language in the field of natural language processing (NLP). Question answering over unstructured text is a challenging NLP task as it requires understanding both question and passage. Very few researchers attempted to perform question answering over Bengali (natively pronounced as Bangla) text. Typically, existing approaches construct the dataset by directly translating them from English to Bengali, which produces noisy and improper sentence structures. Furthermore, they lack topics and terminologies related to the Bengali language and people. This paper introduces BanglaQuAD, a Bengali question answering dataset, containing 30,808 question-answer pairs constructed from Bengali Wikipedia articles by native speakers. Additionally, we propose an annotation tool that facilitates question-answering dataset construction on a local machine. A qualitative analysis demonstrates the quality of our proposed dataset. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()