Asynchronous translations with recurrent neural nets

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Unsupervised Pretraining for Sequence to Sequence Learning (2016, background): “Sequence to sequence (seq2seq) models (Sutskever et al., 2014; Cho et al., 2014; Kalchbrenner and Blunsom, 2013; Allen, 1987; Ñeco and Forcada, 1997) are extremely effective on a variety of tasks that require a mapping between a variable-length input sequence to a variable-length output sequence.”
Transfer Learning for Low-Resource Neural Machine Translation (2016, methodology): “In the neural encoder-decoder framework for MT (Neco and Forcada, 1997; Castaño and Casacuberta, 1997; Sutskever et al., 2014; Bahdanau et al., 2014; Luong et al., 2015a), we use a recurrent neural network (encoder) to convert a source sentence into a dense, fixed-length vector.”
Why Neural Translations are the Right Length (2016, methodology): “1 Introduction The neural encoder-decoder framework for machine translation (Neco and Forcada, 1997; Castaño and Casacuberta, 1997; Sutskever et al., 2014; Bahdanau et al., 2014; Luong et al., 2015) provides new tools for addressing the field’s difficult challenges.”
Multi-Source Neural Translation (2016, methodology): “In the neural encoder-decoder framework for MT (Neco and Forcada, 1997; Castaño and Casacuberta, 1997; Sutskever et al., 2014; Bahdanau et al., 2014; Luong et al., 2015b), we use a recurrent neural network (encoder) to convert a source sentence into a dense, fixed-length vector.”