Optimized Chinese Pronunciation Prediction by Component-Based Statistical Machine Translation
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초록
To eliminate ambiguities in the existing methods to simplify Chinese pronunciation learning, we propose amodel that can predict the pronunciation of Chinese characters automatically. The proposed model relies on astatistical machine translation (SMT) framework. In particular, we consider the components of Chinesecharacters as the basic unit and consider the pronunciation prediction as a machine translation procedure (thecomponent sequence as a source sentence, the pronunciation, pinyin, as a target sentence). In addition totraditional features such as the bidirectional word translation and the n-gram language model, we alsoimplement a component similarity feature to overcome some typos during practical use. We incorporate thesefeatures into a log-linear model. The experimental results show that our approach significantly outperformsother baseline models.
Abstract
To eliminate ambiguities in the existing methods to simplify Chinese pronunciation learning, we propose amodel that can predict the pronunciation of Chinese characters automatically. The proposed model relies on astatistical machine translation (SMT) framework. In particular, we consider the components of Chinesecharacters as the basic unit and consider the pronunciation prediction as a machine translation procedure (thecomponent sequence as a source sentence, the pronunciation, pinyin, as a target sentence). In addition totraditional features such as the bidirectional word translation and the n-gram language model, we alsoimplement a component similarity feature to overcome some typos during practical use. We incorporate thesefeatures into a log-linear model. The experimental results show that our approach significantly outperformsother baseline models.
참고문헌 (18)
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