会议专题

Chinese Grammatical Error Correction Using Statistical and Neural Models

  This paper introduces the Alibaba NLP teams 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.

Grammatical Error Correction Combination Statistical machine translation Neural machine translation

Junpei Zhou Chen Li Hengyou Liu Zuyi Bao Guangwei Xu Linlin Li

Alibaba Group,969 West Wenyi Road,Hangzhou,China;Zhejiang University,38 Zheda Road,Hangzhou,China Alibaba Group,969 West Wenyi Road,Hangzhou,China

国际会议

2018自然语言处理与中文计算国际会议(NLPCC2018)

呼和浩特

英文

117-128

2018-08-26(万方平台首次上网日期,不代表论文的发表时间)