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Seq2seq RNN encoder-decoder with attention mechanism, training Seq2seq RNN encoder-decoder with attention mechanism, training and inferring The attention mechanism is an enhancement introduced by Bahdanau et al. in 2014 to address limitations in the basic Seq2Seq architecture where a longer input sequence results in the hidden state output of ...
One encoder-decoder block A Transformer is composed of stacked encoder layers and decoder layers. Like earlier seq2seq models, the original transformer model used an encoder-decoder architecture. The encoder consists of encoding layers that process all the input tokens together one layer after another, while the decoder consists of decoding ...
A convolutional encoder is called so because it performs a convolution of the input stream with the encoder's impulse responses: = = = [], where x is an input sequence, y j is a sequence from output j, h j is an impulse response for output j and denotes convolution.
Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. [1] [2] It learns to represent text as a sequence of vectors using self-supervised learning. It uses the encoder-only transformer architecture.
The idea of encoder-decoder sequence transduction had been developed in the early 2010s (see previous papers [20] [21]). The papers most commonly cited as the originators that produced seq2seq are two concurrently published papers from 2014. [20] [21] A 380M-parameter model for machine translation uses two long short-term memories (LSTM). [21]
Encoder-decoder RNN with attention mechanism. Two RNNs can be run front-to-back in an encoder-decoder configuration. The encoder RNN processes an input sequence into a sequence of hidden vectors, and the decoder RNN processes the sequence of hidden vectors to an output sequence, with an optional attention mechanism.
In the process of encoding, the sender (i.e. encoder) uses verbal (e.g. words, signs, images, video) and non-verbal (e.g. body language, hand gestures, face expressions) symbols for which he or she believes the receiver (that is, the decoder) will understand. The symbols can be words and numbers, images, face expressions, signals and/or actions.
Additionally, the two networks are also trained to share their hidden representation; this way, the source encoder can produce a representation that the target decoder can decode. [10] Forcada and Ñeco simplified this procedure in 1997 to directly train a source encoder and a target decoder in what they called a recursive hetero-associative ...