Sequence to Sequence

Sequence-to-sequence (Seq2Seq) models are a class of neural networks designed to map input sequences to output sequences, primarily used for tasks like machine translation and text summarization. Current research focuses on improving Seq2Seq performance using transformer architectures, often enhanced with techniques like multi-task learning, knowledge distillation, and in-context learning, and exploring their application in diverse domains beyond natural language processing, including music arrangement, drug discovery, and sensor data analysis. The widespread applicability of Seq2Seq models across various fields highlights their significance in advancing both fundamental understanding of sequence modeling and practical applications requiring efficient and accurate sequence-to-sequence transformations.