Investigating Model Compression for Neural Machine Translation in the Biomedical Domain
Large-scale pretrained transformer models have achieved state-of-the-art performance across diverse machine translation tasks, including multilingual settings. Knowledge distillation has emerged as a sustainable approach for model compression, transferring knowledge from large teacher models to smaller, more efficient student models. Similarly, quantization, which reduces the numerical precision of model weights and activations (e.g., from 32-bit to 8-bit representations) is widely used to accelerate inference, enabling models to run several times faster during deployment. However, both techniques face limitations when applied to specialized domain data, particularly under low-resource co...
| Authors | Maria Zafar, Souhail Bakkali, Rejwanul Haque |
|---|---|
| Primary category | cs.CL |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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