· Deep Learning · 2 min read
📋 Prerequisites
- Basic understanding of transformers and attention
🎯 What You'll Learn
- Understand what the encoder-decoder structure is
- Learn how transformers handle sequence-to-sequence tasks
- Grasp how attention is used in encoder-decoder transformers
- Gain intuition for translation and summarization tasks
Introduction
The transformer encoder-decoder architecture is a powerful design for sequence-to-sequence tasks such as machine translation, summarization, and question answering.
1️⃣ What is the Encoder-Decoder Architecture?
The architecture consists of:
✅ Encoder: Processes the input sequence into a context-rich representation.
✅ Decoder: Generates the output sequence step-by-step using the encoder’s output for context.
2️⃣ Transformer Encoder
The encoder takes input tokens, adds positional embeddings, and processes them through multiple layers consisting of:
- Multi-head self-attention.
- Feed-forward networks.
- Layer normalization and residual connections.
The encoder outputs contextual embeddings representing the entire input sequence.
3️⃣ Transformer Decoder
The decoder:
✅ Takes the encoder’s output as context.
✅ Processes the previously generated tokens with masked self-attention to prevent peeking at future tokens during training.
✅ Uses encoder-decoder attention to align the current decoding step with relevant parts of the input.
The decoder’s output is passed through a linear layer and softmax to predict the next token.
4️⃣ Attention in Encoder-Decoder
a) Self-Attention (Encoder)
Allows each token in the input to attend to every other token, creating context-aware embeddings.
b) Masked Self-Attention (Decoder)
Prevents future token information from leaking during training by masking future positions.
c) Encoder-Decoder Attention
Enables the decoder to focus on relevant parts of the input sequence when generating each output token, essential for accurate translation.
5️⃣ Applications
✅ Machine Translation (e.g., English to French)
✅ Text Summarization
✅ Question Answering
✅ Code Generation
The transformer encoder-decoder architecture is the foundation of models like T5 and MarianMT.
6️⃣ Why Encoder-Decoder Transformers Matter
✅ Handle variable-length input and output sequences efficiently.
✅ Capture long-range dependencies in input-output mappings.
✅ Achieve state-of-the-art results in translation and summarization tasks.
Conclusion
The transformer encoder-decoder architecture:
✅ Separates input processing (encoder) from output generation (decoder).
✅ Uses attention to align input and output efficiently.
✅ Is the backbone of many advanced NLP applications.
What’s Next?
✅ Try using a pretrained encoder-decoder transformer for translation using Hugging Face.
✅ Visualize encoder-decoder attention maps to understand alignments.
✅ Continue structured deep learning on superml.org to build your own translation models.
Join the SuperML Community to share your experiments with encoder-decoder models and learn collaboratively.
Happy Learning! 🌍