Neural Machine Translation

By Minh-Thang Luong. 2016

180 citations recorded by the source corpus.

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Citations in the corpus

Regularized Training Objective for Continued Training for Domain Adaptation in Neural Machine Translation (2018, background): “It consists of three steps: 2 For a detailed explanation of attention based NMT see Bahdanau et al. (2015) (the original paper), or for a gentle introduction see the textbook chapter by Koehn (2017).”
Toward Robust Neural Machine Translation for Noisy Input Sequences (2017, background): “Neural sequence-to-sequence models are known to suffer from explaining-away effects, where models may learn to generate outputs by relying on the target-side context while ignoring the source-side context [10, 11], especially when the source side provides only a weak or noisy signal.”
LIUM Machine Translation Systems for WMT17 News Translation Task (2017, methodology): “A joint Byte Pair Encoding (BPE) model (Sennrich et al., 2016b) with 50K merge operations is learned on the training data .”
Transfer Learning for Low-Resource Neural Machine Translation (2016, unknown): “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.”
On internal language representations in deep learning: an analysis of machine translation and speech recognition (2018, methodology): “The encoder maps a source sentence to a vector representation, which the decoder (19)As noted by Koehn [182], some of these models are remarkably similar to the modem encoder-decoder approach to neural machine translation; see for example figure 1 in [109].”
On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference (2018, background): “Many researchers suggest that state-of-the-art NMT models learn representations that capture the meaning of sentences (Gu et al., 2016; Johnson et al., 2017; Zhou et al., 2017; Andreas and Klein, 2017; Neubig, 2017; Koehn, 2017).”
A Survey of Domain Adaptation for Neural Machine Translation (2018, background): “There are survey papers for NMT (Neubig, 2017; Koehn, 2017); however, they focus on general NMT and more diverse topics.”