Transformer Encoder and Decoder

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! 🌍