An Encoder-Decoder architecture processes input data into a latent representation (encoder) and then generates output from that representation (decoder). It’s widely used in sequence-to-sequence tasks like machine translation and text summarization. Attention mechanisms are often added to improve the model’s focus on relevant input parts.