An encoder-decoder architecture is a neural network design used to transform input data into output data, often for tasks where the input and output are sequences of different lengths. It consists of two main components: the encoder, which processes the input and compresses it into a fixed-size representation (often called a context vector), and the decoder, which uses this representation to generate the output sequence. This architecture is commonly applied to tasks like machine translation, text summarization, or speech recognition, where the goal is to map one structured format to another. For example, translating an English sentence to French requires the model to first “understand” the input sentence and then “generate” the translated version.
The encoder processes the input step-by-step, such as word-by-word in a sentence, and builds a contextual understanding of the entire sequence. In traditional implementations using recurrent neural networks (RNNs), the encoder’s final hidden state serves as the context vector. The decoder then initializes its own hidden state using this vector and generates the output sequence one step at a time. For instance, in machine translation, the encoder might read the English sentence “Hello, how are you?” and produce a context vector capturing its meaning. The decoder uses this vector to generate the French equivalent, “Bonjour, comment ça va?” by predicting each word sequentially. Modern implementations often replace RNNs with Transformer-based models, which use self-attention mechanisms to capture relationships between all input elements simultaneously, improving efficiency and performance.
Practical considerations when using encoder-decoder architectures include handling variable-length sequences and managing computational resources. For example, input sequences might be padded to a fixed length during training, and attention mechanisms are often added to help the decoder focus on relevant parts of the input dynamically. In the Transformer architecture, the encoder and decoder each consist of multiple layers of self-attention and feed-forward networks. Developers might fine-tune pre-trained models like BERT (for encoding) or GPT (for decoding) or use frameworks like TensorFlow or PyTorch to build custom models. Key challenges include avoiding overfitting when training from scratch and optimizing inference speed, especially for long sequences. Tools like beam search can improve output quality during decoding by exploring multiple candidate sequences. Understanding these components helps in adapting the architecture to specific tasks, such as adding domain-specific tokenization for code translation or adjusting attention mechanisms for video captioning.