| Challenge: | Neural machine translation (NMT) has a drawback in that it can generate only high-frequency words owing to the computational costs of the softmax function in the output layer. |
| Approach: | They propose two methods to generate low-frequency words and deal with unknown words using Japanese predicate conjugation information without discarding linguistic information. |
| Outcome: | The proposed methods can generate low-frequency words and deal with unknown words. |
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| Challenge: | Despite above approaches can improve the prediction of rare words, they still have challenges which have adverse effects on its effectiveness. |
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| Challenge: | Neural machine translation (NMT) requires large parallel corpora for training robust and high quality models. |
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| Challenge: | Recent work has shown that optimizing neural machine translation systems to directly improve evaluation metrics such as BLEU can improve final translation accuracy. |
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| Challenge: | Existing studies on incorporating arbitrary syntactic information into neural machine translation (NMT) are lacking. |
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