Contrastive Analysis with Predictive Power: Typology Driven Estimation of Grammatical Error Distributions in ESL
Name
CBMM-Memo-050.pdf
Size
493.74 KB
Format
Adobe PDF
Checksum (MD5)
2caacd92b0d4358bc00b14aa086a197b
Author(s)
Berzak, Yevgeni
Reichart, Roi
Katz, Boris
Date Issued
June 5, 2016
Publisher
Center for Brains, Minds and Machines (CBMM), arXiv
Citation
arXiv:1603.07609v1 [cs.CL]
Series/Report no.
CBMM Memo Series;050
Abstract
This work examines the impact of crosslinguistic 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.
Subjects
English as Second Language (ESL)
Contrastive Analysis (CA)
Computer Language
Terms of Use
Attribution-NonCommercial-ShareAlike 3.0 United States
Persistent DSpace Link
