Abstract
This study solves the problem of mismatch between rigid model and varied morphology in machine translation of agglutinative language in two ways. (1) a free granularity preprocessing strategy is proposed to construct a multi-granularity mixed input. (2) the value iteration network is further added into the reinforcement learning model, and the rewards of each granularity input are converted into decision values, so that the model training has higher target and efficiency. The experimental results show that our approach has achieved significant improvement in the two representative agglutinative language machine translation tasks, including low-resource Mongolian\(\rightarrow \)Chinese, and common Japanese\(\rightarrow \)English, and has greatly shortened the training time.
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