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Recurrent Convolutional Neural Networks for Discourse Compositionality
Sequence to Sequence Learning with Neural Networks
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation – Google Research
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Henkjan de Krijger
Henkjan de Krijger
@henq · 23 Jan 2020
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Hoe werkt Google Translate?

Heel interessant filmpje hoe de vertaalmachine werkt. Voor Yoors gebruiken we overigens Amazon Translate.

#Google
#Translate

Let’s take a look at how Google Translate’s Neural Network works behind the scenes! Read these references below for the best understanding of Neural Machine Translation!

REFERENCES

[1] Landmark paper of LSTM (Hochreiter et al., 1997): https://www.bioinf.jku.at/publication ...
[2] Landmark paper of Neural Machine Translation NMT (Kalchbrenner et al., 2013): https://arxiv.org/abs/1306.3584
[3] Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (Cho et al., 2014): https://arxiv.org/abs/1406.1078
[4] Seq to Seq learning with neural networks (Sutskever et al., 2014): https://arxiv.org/abs/1409.3215)
[5] The paper that introduced Bidirectional RNN : https://pdfs.semanticscholar.org/4b80 ...
[6] On the properties of NMP: Encoder-Decoder Approaches (Cho et al., 2014):https://arxiv.org/pdf/1409.1259.pdf Fig. 4 (a)
[7] NMT by jointly learning to align & translate (Bahdanau et al., 2016): https://arxiv.org/pdf/1409.0473.pdf 5.2.2
[8] Google Translate Main paper (Wu et al., 2016): https://ai.google/research/pubs/pub45610

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