What are sequence-to-sequence models and where are they used?

Sequence-to-sequence models, or seq2seq models, are neural network architectures designed for transforming one sequence into another, making them ideal for tasks like machine translation, text summarization, and conversation modeling. They typically consist of an encoder that processes the input sequence and a decoder that generates the output sequence, often with RNNs, LSTMs, or GRUs. The encoder compresses the input data into a fixed-size context vector, which the decoder uses to produce the output sequence. This architecture handles variable-length input and output sequences effectively.