13.2 Sequence-to-sequence models for machine translation
3 min read•july 25, 2024
Sequence-to-sequence models revolutionize machine translation by transforming input sequences into output sequences. These models use architectures with RNNs, embedding layers, and attention mechanisms to capture complex language relationships and generate accurate translations.
Implementation involves careful consideration of model architecture, training processes, and evaluation metrics. Advanced techniques like , , and further enhance translation quality and efficiency, pushing the boundaries of language understanding and generation.
Sequence-to-Sequence Models for Machine Translation
Architecture of sequence-to-sequence models
Top images from around the web for Architecture of sequence-to-sequence models
Neural Networks Primer - Michał Chromiak's blog View original
Is this image relevant?
The Transformer – Attention is all you need. - Michał Chromiak's blog View original
Is this image relevant?
Transformer Neural Network Architecture View original
Is this image relevant?
Neural Networks Primer - Michał Chromiak's blog View original
Is this image relevant?
The Transformer – Attention is all you need. - Michał Chromiak's blog View original
Is this image relevant?
1 of 3
Top images from around the web for Architecture of sequence-to-sequence models
Neural Networks Primer - Michał Chromiak's blog View original
Is this image relevant?
The Transformer – Attention is all you need. - Michał Chromiak's blog View original
Is this image relevant?
Transformer Neural Network Architecture View original
Is this image relevant?
Neural Networks Primer - Michał Chromiak's blog View original
Is this image relevant?
The Transformer – Attention is all you need. - Michał Chromiak's blog View original
Is this image relevant?
1 of 3
Encoder-Decoder architecture transforms input sequence into output sequence