Papers by Shammur Chowdhury
LAraBench: Benchmarking Arabic AI with Large Language Models (2024.eacl-long)
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Ahmed Abdelali, Hamdy Mubarak, Shammur Chowdhury, Maram Hasanain, Basel Mousi, Sabri Boughorbel, Samir Abdaljalil, Yassine El Kheir, Daniel Izham, Fahim Dalvi, Majd Hawasly, Nizi Nazar, Youssef Elshahawy, Ahmed Ali, Nadir Durrani, Natasa Milic-Frayling, Firoj Alam
| Challenge: | Recent advances in Large Language Models (LLMs) have significantly influenced the landscape of language and speech research. |
| Approach: | They used GPT-3.5-turbo, GPT-4, BLOOMZ, Jais-13b-chat, Whisper, and USM to tackle 33 distinct tasks across 61 datasets. |
| Outcome: | The proposed model outperforms SOTA models in zero-shot learning, with a few exceptions. |
Beyond Orthography: Automatic Recovery of Short Vowels and Dialectal Sounds in Arabic (2024.acl-long)
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| Challenge: | Existing algorithms for recognizing borrowed and dialectal sounds are limited to Arabic, a dialect-rich language containing more than 22 major dialects. |
| Approach: | They propose a framework to recognize borrowed and dialectal sounds within phonologically diverse and dialect-rich languages that extends beyond its standard orthographic sound sets. |
| Outcome: | The proposed framework improves character error rate by 7% with only one and half hours of training data compared to the baseline. |
Automatic Pronunciation Assessment - A Review (2023.findings-emnlp)
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| Challenge: | Pronunciation assessment and its application in computer-aided pronunciation training (CAPT) have seen impressive progress in recent years. |
| Approach: | They review methods employed in computer-aided pronunciation training for both phonemic and prosodic pronunciations. |
| Outcome: | The proposed system should be able to automatically score non-native speech segments and give meaningful feedback. |