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Do L2 Grammars Go Beyond the L1 and L2 Input?
Yu-Chang Liang
139-147 (complete paper or proceedings contents)

Abstract

This study examined English learners' acquisition of Mandarin classifiers to explore whether L2ers still have access to the full inventory of UG in L2A. The authors' results from an AJ task demonstrated that English L2ers at the HI and AD stages successfully acquired the abstract knowledge of Mandarin classifiers (i.e., the ungrammaticality of classifiers modified by adjectives, suffixed with -de and followed by mass nouns), which suggests the projection of CLP and its feature [+count] in their L2 Mandarin grammars. This abstract knowledge is underdetermined by L2 Mandarin input data and unobtainable through their native language English. First, the knowledge is not explicitly taught since it is not in the textbooks, and instructors are not consciously aware of it. Second, the positive evidence alone (e.g., numeral+classifier+count noun) does not inform English L2ers of the ungrammaticality. Third, although indirect negative evidence might inform English L2ers of the ungrammaticality, it is not straightforward due to the fact that the syntactic distribution of measure words is superficially similar to that of classifiers (i.e., numeral+measure word+mass/count noun). Fourth, although English L2ers may receive corrective feedback when ill-formed patterns are produced, it is not reliable and may only temporarily change English L2ers' language behavior, not their L2 Mandarin grammars. Finally, the knowledge cannot come from their L1 English due to the unavailability of classifiers. All of these collectively suggest that there exists a logical problem in English L2ers' acquisition of Mandarin classifiers. The authors' findings provide evidence that UG is operative in L2 grammars.

Published in

Proceedings of the 3rd Conference on Generative Approaches to Language Acquisition North America (GALANA 2008)
edited by Jean Crawford, Koichi Otaki, and Masahiko Takahashi
Table of contents
Printed edition: $320.00