Sentence-Level Readability Assessment for L2 Chinese Learning

InterpretARA: Enhancing Hybrid Automatic Readability Assessment with Linguistic Feature Interpreter and Contrastive Learning

“…Later, statistical machine learning methods have been applied to readability assessment. These methods incorporate some classical machine learning models such as the support vector machines (SVM) into statistical language models (LM) with parsing trees, vocabulary, and features related to semantics and syntax (Lu, Qiu, and Cai 2019). In Lu, Qiu, and Cai (2019), the authors conducted experiments analyzing the impact of 88 linguistic features on sentence complexity.…”

“…These methods incorporate some classical machine learning models such as the support vector machines (SVM) into statistical language models (LM) with parsing trees, vocabulary, and features related to semantics and syntax (Lu, Qiu, and Cai 2019). In Lu, Qiu, and Cai (2019), the authors conducted experiments analyzing the impact of 88 linguistic features on sentence complexity. The results in Lu, Qiu, and Cai (2019) showed that these statistical machine learning models using linguistic features significantly outperform existing readability formulas.…”

Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment

“…The Chinese L1 data sets are textbooks for first language learning for primary school, secondary school, and high- school education from three publishers. The Chinese L2 data sets are from 5 grades of 73 textbooks that are most widely used by 7 universities in China for teaching Chinese to international students, as described in Lu et al (2019). The ENEW data set is of 4 grades of English textbooks from New Concept English series which is one of the most widely used English L2 textbooks in China.…”

“…We also downloaded the OneStopEnglish data set for English L2 learning from its website 5 (Vajjala and Lučić, 2018). Following the feature engineering methodology in previous work (Flesch, 1948;Gunning, 1969;Kincaid et al, 1975;Yang, 1970;Feng, 2010;Jiang et al, 2014;Sung et al, 2015;Qiu et al, 2017;Lu et al, 2019), we design 102 linguistic features for Chinese L1 and 111 features for Chinese L2 readability assessment. We design 33 features for English L2 referencing Vajjala and Meurers (2012).…”

“…For Chinese readability assessment, Sung et al (2015) evaluated 30 linguistic features and classification models with text books in traditional Chinese. Qiu et al (2017), Lu et al (2019), andZhu et al (2019) designed features of different categories for machine learning methods for Chinese L1 and L2 readability assessment at document and sentence levels. Similar works on other languages include French (Todirascu et al, 2016), German (Hancke et al, 2012), Swedish (Pilán et al, 2016), and Japanese (Wang and Andersen, 2016).…”