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, related dataset, so it already knows common language patterns. The model’s early layers learn general features like syntax and semantics, which can be reused when fine‑tuning on a specific task such as machine translation. Because the model begins with a strong foundation, it needs fewer task‑specific examples to reach high accuracy, reducing training time and data requirements. For instance, a language‑model pre‑trained on billions of English sentences can be fine‑tuned on a small set of French‑English sentence pairs, achieving fluent translations with only a few thousand examples. This reuse of learned representations speeds up convergence and improves the model’s overall performance.