Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Two networks in one combined into a seq2seq (sequence to sequence) Encoder-Decoder architecture. RNN Encoder–Decoder with 1000 hidden units. Adadelta optimizer.
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Machine Translation using RNN (Paper) emnlp2014.org
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Sequence Learning using RNN (Slides) slideshare.net
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Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation arxiv.org
In 2016: stacked residual LSTMs with attention mechanisms on encoder/decoder are the best for NMT (Neural Machine Translation).
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Massive Exploration of Neural Machine Translation Architectures arxiv.org
That yields intuition about the boundaries of what works for doing NMT within a framed seq2seq problem formulation.
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Time series forecasting with Sequence-to-Sequence (seq2seq) rnn models github.com
Learn to use a seq2seq model on simple datasets as an introduction to the vast array of possibilities that this architecture offers
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Neural Machine Translation and Sequence-to-sequence Models: A Tutorial arxiv.org
Interesting overview of the subject of NMT, I mostly read part 8 about RNNs with attention as a refresher.
Education › Papers > Recurrent Neural Networks: “Two networks in one combined into a seq2seq (sequence to sequence) Encoder-Decoder architecture. RNN Encoder–Decoder with 1000 hidden units. Adadelta optimizer.”