How does transfer learning enhance the performance of sequence transduction models in natural language processing tasks?

Transfer learning lets a sequence transduction model start with knowledge gained from a large, generic language dataset, so it already knows common grammar and word patterns before learning a specific task. Because the model has already seen many examples, it needs fewer task‑specific examples to fine‑tune and can generalize better. This reduces overfitting, especially for low‑resource languages or small datasets, and speeds up convergence. For instance, a machine‑translation system that first learns to predict next words on a massive corpus can then quickly adapt to translate medical texts with only a few hundred annotated sentences. The result is higher accuracy, faster training, and better handling of rare words.