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Exploiting native language interference for native language identification

Published online by Cambridge University Press:  26 November 2020

Ilia Markov Open the ORCID record for Ilia Markov [Opens in a new window] ,
Vivi Nastase  and
Carlo Strapparava
Ilia Markov*
Affiliation:
University of Antwerp, CLiPS, Antwerp, Belgium
Vivi Nastase
Affiliation:
University of Stuttgart, Stuttgart, Germany
Carlo Strapparava
Affiliation:
FBK-irst, Fondazione Bruno Kessler, Trento, Italy
*
*Corresponding author. E-mail: [email protected]
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Abstract

Native language identification (NLI)—the task of automatically identifying the native language (L1) of persons based on their writings in the second language (L2)—is based on the hypothesis that characteristics of L1 will surface and interfere in the production of texts in L2 to the extent that L1 is identifiable. We present an in-depth investigation of features that model a variety of linguistic phenomena potentially involved in native language interference in the context of the NLI task: the languages’ structuring of information through punctuation usage, emotion expression in language, and similarities of form with the L1 vocabulary through the use of anglicized words, cognates, and other misspellings. The results of experiments with different combinations of features in a variety of settings allow us to quantify the native language interference value of these linguistic phenomena and show how robust they are in cross-corpus experiments and with respect to proficiency in L2. These experiments provide a deeper insight into the NLI task, showing how native language interference explains the gap between baseline, corpus-independent features, and the state of the art that relies on features/representations that cover (indiscriminately) a variety of linguistic phenomena.

Keywords

Native language interferencenative language identificationpunctuationemotionscognates
Type
Article
Information
Natural Language Engineering , Volume 28 , Issue 2 , March 2022 , pp. 167 - 197
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Copyright
© The Author(s), 2020. Published by Cambridge University Press

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