Papers by Mustapha Lebbah
PUMA: Projected Universal Multilingual ASR for Low-Resource Settings. Application to Diverse African Languages (2026.findings-acl)
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| Challenge: | Existing multilingual ASR models fail to generalize to low-resource languages while remaining costly to scale. |
| Approach: | They propose a multilingual ASR model that integrates a learnable language token with acoustic representations to enable language-aware processing. |
| Outcome: | The proposed model improves low-resource performance with reduced model complexity on African languages. |
Leveraging Text-to-Text Transformers as Classifier Chain for Few-Shot Multi-Label Classification (2025.emnlp-main)
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| Challenge: | Multilabel text classification (MLTC) is an essential task in NLP applications. |
| Approach: | They propose a distillation-based T5 generalist model for zero-shot MLTC and few-shot fine-tuning. |
| Outcome: | The proposed model outperforms baselines of similar size on three few-shot tasks. |
Enhancing Few-Shot Topic Classification with Verbalizers. a Study on Automatic Verbalizer and Ensemble Methods (2024.lrec-main)
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Quang Anh Nguyen, Nadi Tomeh, Mustapha Lebbah, Thierry Charnois, Hanene Azzag, Santiago Cordoba Muñoz
| Challenge: | Pretrained language models are increasingly being used for many tasks. |
| Approach: | They propose to use verbalizers to help interpret masked word distributions into output predictions. |
| Outcome: | The proposed approach outperforms models trained with individual templates while using significantly less resources. |