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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