Papers by Tasnim Kabir
AUDITA: A New Dataset to Audit Humans vs. AI Skill at Audio QA (2026.findings-acl)
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| 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%. |
You Make me Feel like a Natural Question: Training QA Systems on Transformed Trivia Questions (2024.emnlp-main)
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Tasnim Kabir, Yoo Yeon Sung, Saptarashmi Bandyopadhyay, Hao Zou, Abhranil Chandra, Jordan Boyd-Graber
| Challenge: | Existing question-answering datasets are expensive and difficult to annotate and time-consuming to gather. |
| Approach: | They propose to transform Manchester questions into web queries using the same question datasets. |
| Outcome: | The proposed questions can be trained on a Manchester QA dataset using the Quiz Bowl (QB) sample. |