An encoder-decoder architecture is a framework commonly used in machine learning and neural networks, especially for tasks that involve transforming input data into a different format or representation. This architecture is primarily employed in sequence-to-sequence (seq2seq) tasks, where the input and output are both sequences. The architecture consists of two main components: the encoder and the decoder. The encoder processes the input data and compresses the information into a fixed-size context vector, which serves as a summary of the input. On the other hand, the decoder takes this context vector and generates the output sequence step by step.
In practice, the encoder is typically implemented using recurrent neural networks (RNNs), long short-term memory networks (LSTMs), or more recent approaches like gated recurrent units (GRUs) and transformer models. For example, in a machine translation task, the encoder reads a sentence in the source language and transforms it into a context vector that captures its meaning. The decoder then generates the corresponding sentence in the target language, word by word, based on the information provided by the encoder. This two-step process enables the model to handle complex transformations between different types of sequences.
The encoder-decoder architecture can be extended in various ways. For instance, attention mechanisms are often integrated to allow the decoder to focus on different parts of the input sequence at each decoding step, rather than relying solely on a single context vector. This improvement helps in better handling longer sequences and enhances the quality of the generated output. Applications of this architecture extend beyond language translation to other areas such as image captioning, text summarization, and speech recognition, making it a versatile tool for developers working with neural networks.