Papers by Tasnim Ahmed
AUDITA: A New Dataset to Audit Humans vs. AI Skill at Audio QA (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing audio question answering benchmarks emphasize sound event classification or caption-grounded queries. |
| Approach: | They propose a large-scale, real-world audio question answering benchmark to evaluate audio reasoning beyond surface-level acoustic recognition. |
| Outcome: | The proposed model achieves 32.13% accuracy while demonstrating comprehension of audio . state-of-the-art models perform poorly, with average accuracy below 8.86%. |
NERvous About My Health: Constructing a Bengali Medical Named Entity Recognition Dataset (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is used in a variety of downstream tasks in the biomedical domain, but is difficult when working with consumer health questions (CHQs). |
| Approach: | They propose to use a dataset to identify named entities in health-related texts in Bengali to address the scarcity of available data. |
| Outcome: | The proposed dataset captures the diverse range of linguistic styles and dialects used by native speakers from various regions in their day-to-day lives. |