Improving Chinese Grammatical Error Correction with Corpus Augmentation and Hierarchical Phrase-based Statistical Machine Translation

Abstract

In this study, we describe our system submitted to the 2nd Workshop on Natural Language Processing Techniques for Educational Applications (NLP-TEA-2) shared task on Chinese grammatical error diagnosis (CGED). We use a statistical machine translation method already applied to several similar tasks (Brockett et al., 2006; Chiu et al., 2013; Zhao et al., 2014). In this research, we examine corpus-augmentation and explore alternative translation models including syntaxbased and hierarchical phrase-based models. Finally, we show variations using different combinations of these factors.