Papers with PUMA
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. |
SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPC (2024.findings-acl)
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| Challenge: | a growing number of cloud-based inference services are relying on SMPC to protect data privacy. |
| Approach: | They propose a framework for Privacy-Preserving Inference for Transformer models that eliminates exponential and maximum operations in PPI without sacrificing model performance. |
| Outcome: | The proposed framework outperforms MPCFormer in terms of performance and efficiency . it is 3.57 and 3.58 times faster than PUMA for BERTBASE and BERTLARGE . |