Mar 10, 2021 · Multiple RNN cells can be stacked together to form the encoder. RNN reads each inputs sequentially. For every timestep (each input) t, the hidden state (hidden vector) h is updated according...

Aug 7, 2019 · The Encoder-Decoder architecture with recurrent neural networks has become an effective and standard approach for both neural machine translation (NMT) and sequence-to-sequence (seq2seq) prediction in general.

Feb 1, 2021 · In the encoder-decoder model, the input sequence would be encoded as a single fixed-length context vector. We will obtain a context vector that encapsulates the hidden and cell state of the...

Figure 10.3 Basic RNN-based encoder-decoder architecture. The final hidden state of the encoder RNN serves as the context for the decoder in its role as h 0 in the decoder RNN.

Aug 14, 2019 · RNN Encoder-Decoder, consists of two recurrent neural networks (RNN) that act as an encoder and a decoder pair. The encoder maps a variable-length source sequence to a fixed-length vector, and the decoder maps the vector representation back to a variable-length target sequence.

Encoder-decoder architectures can handle inputs and outputs that both consist of variable-length sequences and thus are suitable for sequence-to-sequence problems such as machine translation. The encoder takes a variable-length sequence as input and transforms it into a state with a fixed shape.

RNNs use an Encoder-Decoder approach to handle tasks like translation, Q&A systems, and chatbot training. Let’s break down both phases: In the encoder phase: We train the model with...

Figure 8.17 Translating a single sentence (inference time) in the basic RNN version of encoder-decoder ap-proach to machine translation. Source and target sentences are concatenated with a separator token in between, and the decoder uses context …

In contrast to DNNS, RNNs are capable of modeling a mapping to a variable number of target vectors. Let's dive a bit deeper into the functioning of RNN-based encoder-decoder models....

Bengio et al. 2006 introduced a neural language model for learning word representations. They can be approximated to dense matrices using SVD. Most popular model nowadays is Google’s skip-gram word2vec [Mikolov et al. 2013]. The idea is to predict context words of a given word. c0 is intractable, but negative sampling does the job.