Neural Machine Translation (NMT) Task

Context:
- It can be solved by a Machine Learning Translation System that implements a Machine Learning Translation Algorithm.
Example(s):
- a Google's NMT Task (Wu et al., 2016).
- a Neural Machine Translation Task such as:
  - a Sennrich-Haddow-Birch Rare Words Neural Machine Translation Task (Sennrich et al.,2016).
  - a Bahdanau-Cho-Bengio Neural Machine Translation Task (Bahdanau et al.,2015).
- …
Counter-Example(s):
- a Phrase-based MT Task.
See: Language Model, Statistical MT Task, Neural seq2seq Task, Neural Text Generation Task, Natural Language Processing Task, Neural Encoder-Decoder Task.

References

(Wu et al., 2016) ⇒ Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, Jeffrey Dean. (2016). “Google's Neural Machine Translation System: Bridging the Gap Between Human and Machine Translation.” arXiv preprint arXiv:1609.08144
- QUOTE: Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Unfortunately, NMT systems are known to be computationally expensive both in training and in translation inference. Also, most NMT systems have difficulty with rare words. .