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🌐 Sequence-to-Sequence Translation with Attention

See how an encoder-decoder RNN translates one language into another, and how an attention mechanism frees the decoder from squeezing an entire sentence into a single fixed-size vector.

Machine Learning & Neural Networks3DModerate60 FPS

The simulation animates an encoder RNN reading a source sentence word by word into a chain of hidden states, then a decoder generating the translated sentence step by step while an attention heatmap lights up showing which source words the decoder is weighting most heavily at each moment.

🔬 What It Demonstrates

🎮 How to Use

Choose a sentence pair from the dropdown, use the speed slider to control how fast the encoder and decoder advance, and watch the attention heatmap update at each decoding step; press Rebuild to load a new sentence pair or Pause to freeze the animation and inspect the current attention weights.

💡 Did You Know?

The attention mechanism from this exact architecture, originally just a fix for the fixed-size context vector bottleneck in RNN translation, is the direct conceptual ancestor of the self-attention layers powering every modern large language model, after the 2017 Transformer paper removed the recurrence entirely and kept only the attention.

⚙ Under the hood

Watch a decoder generate a translation one token at a time while attention weights shift across every encoder token, replacing a single fixed-size bottleneck vector.

seq2seqattentionmachine translationencoder-decoderrnnmachine-learningnlp

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install