Building a TOCFL Learner Corpus for Chinese Grammatical Error Diagnosis

Abstract

This study describes the construction of a TOCFL learner corpus and its usage for Chinese grammatical error diagnosis. We collected essays from the Test Of Chinese as a Foreign Language (TOCFL) and annotated grammatical errors using hierarchical tagging sets. Two kinds of error classifications were used simultaneously to tag grammatical errors. The first capital letter of each error tags denotes the coarse-grained surface differences, while the subsequent lowercase letters denote the fine-grained linguistic categories. A total of 33,835 grammatical errors in 2,837 essays and their corresponding corrections were manually annotated. We then used the Standard Generalized Markup Language to format learner texts and annotations along with learners’ accompanying metadata. Parts of the TOCFL learner corpus have been provided for shared tasks on Chinese grammatical error diagnosis. We also investigated systems participating in the shared tasks to better understand current achievements and challenges. The datasets are publicly available to facilitate further research. To our best knowledge, this is the first annotated learner corpus of traditional Chinese, and the entire learner corpus will be publicly released.

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