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Neural machine translation by jointly learning to align and translate
Neural machine translation by jointly learning to align and translate. -
Various Machine Translation datasets
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used various datasets for machine translation tasks. -
Moses Toolkit dataset
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the Moses toolkit to tokenize sentences and split words into subword units. -
IT, Koran, Medical, and Law datasets
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used four commonly-used benchmarks, including IT, Koran, Medical, and Law. -
NIST Chinese-English
The dataset used for the experiments of simultaneous neural machine translation. -
WMT15 English-German
The dataset used for the experiments of simultaneous neural machine translation. -
IWSLT16 German-English
The dataset used for the experiments of simultaneous neural machine translation. -
IWSLT 2015 English-Vietnamese
The IWSLT 2015 English-Vietnamese language data set, which has around 133k training sentence pairs. -
Recurrent Continuous Translation Models
A neural machine translation toolkit that uses maximum likelihood as the training criterion. -
XNMT: The eXtensible Neural Machine Translation Toolkit
XNMT is a neural machine translation toolkit that focuses on modular code design, making it easy to swap in and out different parts of the model. -
French-English Translation Task
The dataset used in the paper is a French-English translation task. -
Unsupervised Neural Machine Translation with Weight Sharing
The proposed approach is tested on English-German, English-French and Chinese-to-English translation tasks.