NCTB-QA: A Large-Scale Bangla Educational Question Answering Dataset and Benchmarking Performance
Reading comprehension systems for low-resource languages face significant challenges in handling unanswerable questions. These systems tend to produce unreliable responses when correct answers are absent from context. To solve this problem, we introduce NCTB-QA, a large-scale Bangla question answering dataset comprising 87,805 question-answer pairs extracted from 50 textbooks published by Bangladesh's National Curriculum and Textbook Board. Unlike existing Bangla datasets, NCTB-QA maintains a balanced distribution of answerable (57.25%) and unanswerable (42.75%) questions. NCTB-QA also includes adversarially designed instances containing plausible distractors. We benchmark three transform...
| Authors | Abrar Eyasir, Tahsin Ahmed, Muhammad Ibrahim |
|---|---|
| Primary category | cs.CL |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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