Abstract
This study solves the problem of unknown(UNK) word in machine translation of agglutinative language in two ways. (1) a multi-granularity preprocessing based on morphological segmentation is used for the input of generative adversarial net. (2) a filtering mechanism is further used to identify the most suitable granularity for the current input sequence. The experimental results show that our approach has achieved significant improvement in the two representative agglutinative language machine translation tasks, including Mongolian\(\rightarrow \)Chinese and Japanese\(\rightarrow \)English.
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Ji, Y., Hou, H., Chen, J., Wu, N. (2019). Noise-Based Adversarial Training for Enhancing Agglutinative Neural Machine Translation. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11670. Springer, Cham. https://doi.org/10.1007/978-3-030-29908-8_31
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DOI: https://doi.org/10.1007/978-3-030-29908-8_31
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