Abstract
Real-world business applications require a trade-off between language model performance and size. We propose a new method for model compression that relies on vocabulary transfer. We evaluate the method on various vertical domains and downstream tasks. Our results indicate that vocabulary transfer can be effectively used in combination with other compression techniques, yielding a significant reduction in model size and inference time while marginally compromising on performance.
Type
Publication
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
Paolo Torroni
Associate Professor
Head of the Language Technologies lab. His main research focus is in artificial intelligence, and in particular natural language processing, multi-agent systems, and computational logics.