How syntactic complexity indices predict Chinese L2 writing quality: An analysis of unified dependency syntactically-annotated corpus

Abstract

Previous syntactic complexity (SC) research on L2 Chinese has overlooked a range of Chinese-specific structures and fine-grained indices. This study, utilizing a syntactically annotated Chinese L2 writing corpus, simultaneously employs both large-grained and fine-grained syntactic complexity indices to investigate the relationship between syntactic complexity and writing quality produced by English-speaking Chinese second language (ECSL) learners from macro and micro perspectives. Our findings reveal the following: (a) at a large-grained level of analysis using syntactic complexity indices, the generic syntactic complexity indice (GSC indice) number of T-units per sentence and the Chinese-specific syntactic complexity indice (CSC indice) number of Clauses per topic chain unit account for 14.5% of the total variance in writing scores among ECSL learners; (b) the syntactic diversity model alone accounts for 24.7% of the variance in Chinese writing scores among ECSL learners; (c) the stepwise regression analysis model, which integrates fine-grained SC indices extracted from the syntactically annotated corpus, explains 43.7% of the variance in Chinese writing quality. This model incorporates CSC indices such as average ratio of dependency types per 30 dependency segments, the ratio of adjuncts to sentence end, the ratio of predicate complements, the ratio of numeral adjuncts, the mean length of Topic-Comment-Unit dependency distance, as well as GSC indices like the ratio of main governors, the ratio of attributers, the ratio of coordinating adjuncts, and the ratio of sentential objects. These findings highlight the valuable insights that syntactically annotated fine-grained SC indices offer regarding the writing characteristics of ECSL learners.