Developing learner corpus annotation for Chinese grammatical errors

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.

Overview of the NLP-TEA 2015 Shared Task for Chinese Grammatical Error Diagnosis

“…The learner corpora used in our shared task were taken from two sources: the writing section of the computer-based Test Of Chinese as a Foreign Language (TOCFL) (Lee et al, 2016) and the writing section of the Hanyu Shuiping Kaoshi(HSK, Test of Chinese Level) (Cui et al, 2011;Zhang et al, 2013). Native Chinese speakers were trained to manually annotate grammatical errors and provide corrections corresponding to each error.…”

A Cognitive Diagnostic Module Based on the Repair Theory for a Personalized User Experience in E-Learning Software

“…Concerning language learning, there have been several research efforts that present the error diagnosis process can diagnose, among others, grammatical, syntactic, vocabulary mistakes by using techniques, such as approximate string matching, convolutional sequence to sequence modeling, context representation, etc. [13][14][15][16][17][18][19]. For example, the work of [19] proposes a sequence-to-sequence learning approach using recurrent neural networks for conducting error analysis and diagnosis.…”

“…In the work of [14], the authors used the Clause Complex model to analyze the learners' errors emerging from grammatical differences in language learning. The work of [15] proposes a framework of hierarchical tagging sets to perform annotation of grammatical mistakes in language learning. Finally, the authors of [16] performed classification on spelling mistakes in two categories, i.e., orthographic and phonological errors.…”

Building a Japanese Typo Dataset from Wikipedia’s Revision History

“…For over a decade, user generated content (UGC) has been an important target of NLP technology. It is characterized by phenomena not found in standard texts, such as word lengthening (Brody and Diakopoulos, 2011), dialectal variations (Saito et al, 2017;Blodgett et al, 2016), unknown onomatopoeias (Sasano et al, 2013), grammatical errors (Mizumoto et al, 2011;Lee et al, 2018), and mother tongue interference in non-native writing (Goldin et al, 2018). Typographical errors (typos) also occur often in UGC.…”