Seq2Seq
A neural network framework designed to transform an input sequence of varying length into a completely new output sequence of varying length.
Think of It Like This
Like a professional translator listening to a long, winding speech in English and delivering a concise, structured summary in Spanish.
Originally popularized using recurrent neural networks (RNNs) for machine translation, the Sequence-to-Sequence paradigm uses an Encoder to compress the input into a context vector, and a Decoder to generate the output. It is the foundational architecture for tasks like translation, summarization, and speech recognition.