Encoder-Decoder Architecture

The encoder-decoder architecture is commonly used for sequence-to-sequence tasks, such as Machine Translation, Summarization, and dialogue generation. It allows us to transform an input sequence (e.g., a sentence in one language) into an output sequence (e.g., its translation in another language) or generate a response based on the input.


  • Machine translation: The encoder reads the source language sentence, and the decoder generates the corresponding sentence in the target language.
  • Text summarization: The encoder processes a lengthy piece of text, capturing its key points. The decoder condenses this information into a shorter, informative summary.
  • Question answering: The encoder reads a passage of text and the question, while the decoder generates the answer based on the understanding of both.
  • Image captioning: The encoder analyzes an image, extracting its visual features. The decoder uses these features to generate a textual description of the image content.
  • Speech recognition and synthesis: These applications can be framed as sequence-to-sequence problems. Encoders can be used to convert speech into a sequence of features, which are then decoded back into text by the decoder. Similarly, text can be encoded and decoded into speech.


  • Transformers:
    • Transformers rely on self-attention mechanisms instead of recurrent connections, achieving state-of-the-art results in machine translation and other tasks.
    • Transformer Models:
      • BERT (Bidirectional Encoder Representations from Transformers): BERT is a popular natural language processing model that uses a multi-layer bidirectional Transformer encoder to generate contextualized representations of input sequences. BERT can be fine-tuned for a variety of natural language processing tasks such as question answering, sentiment analysis, and named entity recognition.
      • GPT (Generative Pre-trained Transformer): GPT is a generative language model that uses a multi-layer Transformer decoder to generate text. GPT is trained on a large corpus of text data and can be fine-tuned for a variety of natural language processing tasks such as text classification, summarization, and translation.
      • T5 (Text-to-Text Transfer Transformer): T5 is a Transformer-based model that treats every natural language processing task as a text-to-text problem. T5 uses a similar architecture to BERT but with a few key differences, such as the use of a text-based input format and a denoising autoencoder pretraining objective.
  • Sequence-to-Sequence with Attention (Seq2Seq with Attention)
    • This builds upon RNNs by introducing an attention mechanism. The attention mechanism allows the decoder to focus on specific parts of the encoded input sequence, leading to more accurate outputs.
    • Applications:
      • Machine Translation
      • Text Summarization
      • Dialogue Systems
      • Image Captioning
  • Variational Autoencoders (VAE):
  • Recurrent Neural Networks (RNN) with LSTM or GRU cells
  • Convolutional Encoder-Decoder and Seq2Seq Models:
    • Applications:
      • Semantic Segmentation
      • Image-to-Image Translation
      • Video Frame Prediction
  • Autoregressive Models

Stages and processes:

  1. Encoder Stage:
    • The encoder processes the input sequence (usually a sequence of words) and produces a fixed-length vector representation (also known as the context vector).
    • Two common implementations for the encoder stage are:
      • Recurrent Neural Networks (RNNs): RNNs process the input sequence sequentially, updating hidden states at each time step. The final hidden state serves as the context vector.
      • Transformer Blocks: Transformers use self-attention mechanisms to capture contextual information across the entire input sequence. The encoder consists of multiple layers of self-attention and feed-forward neural networks.
  2. Decoder Stage:
    • The decoder takes the context vector from the encoder and generates the output sequence (e.g., translated text or a response).
    • Similar to the encoder, there are two common implementations for the decoder stage:
      • RNNs: The decoder RNN generates the output sequence word by word, using the context vector and previously generated tokens as input.
      • Transformer Blocks: The decoder in a transformer model also uses self-attention and feed-forward layers. It predicts the next word based on the context vector and previously generated tokens.

Training an encoder-decoder architecture:

  • Training an encoder-decoder model involves using a dataset with input-output pairs (e.g., English sentences paired with their French translations).
  • During training:
    • The input sentence is fed to the encoder, which produces the context vector.
    • The decoder generates the output sequence one token at a time, conditioned on the context vector and previously generated tokens.
    • The model’s weights are adjusted through backpropagation to minimize the difference between predicted and actual output tokens.

Encoder-decoder serving process:

  1. Load the saved checkpoint, initializing the encoder and decoder with the learned weights
  2. Provide an input sentence or prompt.
  3. Prepare the input data (e.g., tokenize and encode the source sequence).
  4. Pass the input through the encoder and produces the context vector.
  5. Initialize the decoder with the context vector and a start-of-sequence token.
  6. The decoder starts generating the output sequence word by word, using the context vector and previously generated tokens.
    • Decoding Strategies
      • Greedy Decoding:
        • In greedy decoding, we select the most likely token (word) at each time step based on the model’s output probabilities.
        • At each step, we choose the token with the highest probability, assuming it’s the best choice.
        • While computationally efficient, greedy decoding may lead to suboptimal results because it doesn’t consider future context.
      • Beam Search:
        • Beam search is a more sophisticated approach that maintains a “beam” of top-k partial sequences, , exploring multiple possibilities.
          • At each time step, it expands the beam by considering the k most likely next tokens for each partial sequence.
          • The combined probabilities of the partial sequences are used to rank and select the k best candidates.
          • Beam search explores multiple paths, accounting for global context, and tends to produce more coherent and contextually accurate output.
  7. Repeat until an end-of-sequence token is generated or a maximum length is reached.
  8. Decode the output tokens (e.g., detokenize for text generation) and present them.

Learning Material: