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- W2885779207 abstract "This paper introduces the Alibaba NLP team’s system for NLPCC 2018 shared task of Chinese Grammatical Error Correction (GEC). Chinese as a Second Language (CSL) learners can use this system to correct grammatical errors in texts they wrote. We proposed a method to combine statistical and neural models for the GEC task. This method consists of two modules: the correction module and the combination module. In the correction module, two statistical models and one neural model generate correction candidates for each input sentence. Those two statistical models are a rule-based model and a statistical machine translation (SMT)-based model. The neural model is a neural machine translation (NMT)-based model. In the combination module, we implemented it in a hierarchical manner. We first combined models at a lower level, which means we trained several models with different configurations and combined them. Then we combined those two statistical models and a neural model at the higher level. Our system reached the second place on the leaderboard released by the official." @default.
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- W2885779207 date "2018-01-01" @default.
- W2885779207 modified "2023-10-16" @default.
- W2885779207 title "Chinese Grammatical Error Correction Using Statistical and Neural Models" @default.
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- W2885779207 doi "https://doi.org/10.1007/978-3-319-99501-4_10" @default.
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