A Sequence to Sequence Learning for Chinese Grammatical Error Correction

被引：22

作者：

Ren, Hongkai ^{[1
,2
]}

Yang, Liner ^{[1
,2
]}

Xun, Endong ^{[1
,2
]}

机构：

[1] Beijing Adv Innovat Ctr Language Resources, Beijing, Peoples R China

[2] Beijing Language & Culture Univ, Sch Informat Sci, Beijing, Peoples R China

来源：

NATURAL LANGUAGE PROCESSING AND CHINESE COMPUTING, NLPCC 2018, PT II | 2018年 / 11109卷

关键词：

Grammatical Error Correction; Convolutional Sequence to Sequence Model; Neural machine translation;

D O I：

10.1007/978-3-319-99501-4_36

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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 F-0.5 score is 29.02. Our final results ranked third among the participants.

引用

页码：401 / 410

页数：10

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