A Sequence to Sequence Learning for Chinese Grammatical Error Correction

摘要：

　　Grammatical Error Correction(GEC)is an important task in natural language processing.In this paper,we introduce our system on NLPCC 2018 Shared Task 2 Grammatical Error Correction.The task is to detect and correct grammatical errors that occurred in Chinese essays written by non-native speakers of Mandarin Chinese.Our system is mainly based on the convolutional sequence-to-sequence model.We regard GEC as a translation task from the language of “bad Chinese to the language of “good Chinese.We describe the building process of the model in details.On the test data of NLPCC 2018 Shared Task 2,our system achieves the best precision score,and the F0.5 score is 29.02.Our final results ranked third among the participants.

关键词： Grammatical Error Correction Convolutional Sequence to Sequence Model Neural machine translation

作者: Hongkai Ren Liner Yang Endong Xun

作者单位: Beijing Advanced Innovation Center for Language Resources,Beijing,China;School of Information Science,Beijing Language and Culture University,Beijing,China

会议类型: 国际会议

会议名称: 2018自然语言处理与中文计算国际会议(NLPCC2018)

会议地点: 呼和浩特

会议语种:英文

页码: 401-410

在线出版日期: 2018-08-26（万方平台首次上网日期，不代表论文的发表时间）