Contrastive Analysis with Predictive Power: Typology Driven Estimation of Grammatical Error Distributions in ESL
This addresses the challenge of understanding cross-linguistic transfer effects in second language acquisition for ESL learners and educators, offering a novel predictive tool that is incremental in applying computational methods to linguistic theory.
This work tackles the problem of predicting grammatical error distributions in English as Second Language (ESL) texts by using a computational framework based on Contrastive Analysis, which leverages typological properties of native languages relative to English, achieving accurate estimates without requiring ESL data for target languages.
This work examines the impact of cross-linguistic transfer on grammatical errors in English as Second Language (ESL) texts. Using a computational framework that formalizes the theory of Contrastive Analysis (CA), we demonstrate that language specific error distributions in ESL writing can be predicted from the typological properties of the native language and their relation to the typology of English. Our typology driven model enables to obtain accurate estimates of such distributions without access to any ESL data for the target languages. Furthermore, we present a strategy for adjusting our method to low-resource languages that lack typological documentation using a bootstrapping approach which approximates native language typology from ESL texts. Finally, we show that our framework is instrumental for linguistic inquiry seeking to identify first language factors that contribute to a wide range of difficulties in second language acquisition.