Sequence-to-Sequence Learning

A general modeling approach focused on converting one input sequence into another output sequence.

Sequence-to-sequence learning provides a general framework for tasks such as translation, summarization, speech-to-text, and format conversion. The goal is not merely to predict a class, but to generate a structured output sequence. It has remained one of the main axes of sequence modeling from the RNN era to the Transformer age. It is a central concept for understanding structured generation problems.