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What is transformer attention mechanism?
The transformer is a new encoder-decoder architecture that uses only the attention mechanism instead of RNN to encode each position, to relate two distant words of both the inputs and outputs w.r.t. itself, which then can be parallelized, thus accelerating the training.
What is attention in encoder-decoder?
Attention is a mechanism that addresses a limitation of the encoder-decoder architecture on long sequences, and that in general speeds up the learning and lifts the skill of the model no sequence to sequence prediction problems.
What is encoder and decoder in transformer?
Transformer Model. On a high level, the encoder maps an input sequence into an abstract continuous representation that holds all the learned information of that input. The decoder then takes that continuous representation and step by step generates a single output while also being fed the previous output.
How is a transformer different from an encoder and decoder?
The Transformer architecture was introduced in 2017. Like the encoder-decoder architectures, where input sequences are fed into the Encoder, and Decoder will predict each word after another. The Transformer improves its time complexity and performance by eliminating RNN and utilizing the attention mechanism.
How does a transformer work in an attention network?
The transformer architecture continues with the Encoder-Decoder framework that was a part of the original Attention networks — given an input sequence, create an encoding of it based on the context and decode that context-based encoding to the output sequence.
Why do we need masking in Transformers decoder?
Masking is needed to prevent the attention mechanism of a transformer from “cheating” in the decoder when training (on a translating task for instance). This kind of “ cheating-proof masking” is not present in the encoder side. I had a tough time understanding how masking was done in the decoder, that is why I’m writing this article.
Which is the evolution of the encoder-decoder model?
The Transformer model is the evolution of the encoder-decoder architecture, proposed in the paper Attention is All You Need. While encoder-decoder architecture has been relying on recurrent neural networks (RNNs) to extract sequential information, the Transformer doesn’t use RNN.