Sequence to Sequence Task

Sequence-to-sequence tasks involve mapping input sequences to output sequences, a fundamental problem in areas like machine translation and text summarization. Current research focuses on improving efficiency and accuracy using various architectures, including Transformers, RNN-Transducers, and novel approaches like diffusion models and attention-based methods, often incorporating techniques like knowledge distillation and parameter-efficient fine-tuning. These advancements aim to enhance model performance, particularly for long sequences and resource-constrained environments, leading to improved applications in natural language processing and other fields requiring sequential data processing.