Supervised neural machine translation based on data augmentation and improved training & inference process (D19-52)
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| Challenge: | This paper describes the neural machine translation systems for the shared translation tasks of WAT 2019 . |
| Approach: | They propose a model for translation tasks of WAT 2019 that employs a Transformer model as the baseline and a deep layer model to improve translation quality. |
| Outcome: | The proposed methods can improve translation quality over traditional statistical machine translation (SMT) The proposed models can improve the translation quality of Japanese-English and Japanese-Chinese corpus. |
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| Challenge: | Neural machine translation (NMT) has achieved stateof-the-art performance on various language pairs. |
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| Challenge: | Neural machine translation models often rely on large-scale parallel corpora for training, exhibiting degraded performance on low-resource languages. |
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Benjamin Marie, Hour Kaing, Aye Myat Mon, Chenchen Ding, Atsushi Fujita, Masao Utiyama, Eiichiro Sumita
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