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Encoder-decoder architecture is a fundamental deep learning model used for sequence-to-sequence tasks. It consists of two main components: an encoder and a decoder. The encoder processes the input sequence and compresses it into a fixed-length vector representation, often called the context vector or thought vector. This vector encapsulates the essential information from the input. The decoder then takes this context vector and generates the output sequence, one element at a time.

The encoder is typically a recurrent neural network (RNN) such as a Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU). It reads the input sequence step by step, updating its internal state at each step. The final hidden state of the encoder serves as the context vector. The decoder is also an RNN, initialized with the encoder's context vector. At each time step, it takes the context vector and the previously generated output element (or a special start token for the first step) as input to predict the next element in the output sequence.

This architecture is highly versatile and has been applied to a wide range of natural language processing (NLP) tasks, including machine translation, text summarization, question answering, and image captioning. In machine translation, the encoder processes the source language sentence, and the decoder generates the target language sentence. For text summarization, the encoder reads the original document, and the decoder produces a concise summary.

A significant advancement in encoder-decoder models is the incorporation of attention mechanisms. Attention allows the decoder to selectively focus on different parts of the input sequence at each step of generating the output. This overcomes the limitation of the fixed-length context vector, especially for long input sequences, by providing a dynamic focus. Attention has dramatically improved the performance of encoder-decoder models across various sequence-to-sequence tasks.