Neural machine translation: Challenges, progress and future
نویسندگان
چکیده
منابع مشابه
Six Challenges for Neural Machine Translation
We explore six challenges for neural machine translation: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search. We show both deficiencies and improvements over the quality of phrasebased statistical machine translation.
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IT is now twelve years since I tentatively suggested that it should be possible to translate a foreign language by means of a digital computer. During this period many people have started to work on the idea, and we have now reached a stage when at least in theory it would be possible to set about building a special purpose translating machine. It may be interesting, therefore, to survey how ta...
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In recent years there has been an enormous boom in MT research. There has been not only an increase in the number of research groups in the field and in the amount of funding, but there is now also optimism for the future of the field and for achieving even better quality. The major reason for this change has been a paradigm shift away from linguistic/rule-based methods towards empirical/data-d...
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In order to control computational complexity, neural machine translation (NMT) systems convert all rare words outside the vocabulary into a single unk symbol. Previous solution (Luong et al., 2015) resorts to use multiple numbered unks to learn the correspondence between source and target rare words. However, testing words unseen in the training corpus cannot be handled by this method. And it a...
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ژورنال
عنوان ژورنال: Science China Technological Sciences
سال: 2020
ISSN: 1674-7321,1869-1900
DOI: 10.1007/s11431-020-1632-x