Seq2seq Model

Sequence-to-sequence (Seq2seq) models are neural network architectures designed to translate input sequences into output sequences, addressing tasks like machine translation and text summarization. Current research focuses on improving Seq2seq performance through architectural innovations (e.g., Transformers, LSTMs) and training methodologies such as bidirectional awareness induction and knowledge distillation, particularly for low-resource scenarios. These advancements are significantly impacting various fields, enabling improvements in natural language processing, medical image analysis, and other areas requiring sequence-to-sequence transformations.