Encoder-Decoder Model

Encoder-Decoder Model is a neural network architecture that processes input sequences through an encoder component that compresses information into fixed-size representations, then generates output sequences through a decoder component that reconstructs or translates the encoded information. This two-stage design enables sequence-to-sequence learning where input and output lengths can differ significantly, making it ideal for tasks like machine translation, text summarization, and speech recognition. The encoder transforms variable-length input sequences into context-rich vector representations, while the decoder generates target sequences by conditioning on the encoded representations and previously generated tokens. Modern encoder-decoder implementations incorporate attention mechanisms that allow decoders to focus on relevant encoder states during generation, improving translation quality and handling long sequences effectively. Advanced variants include transformer-based encoders and decoders that leverage self-attention for superior performance across diverse language processing applications.