Using corpora from Natural Language Processing for investigating crosslinguistic influence

Abstract

Language transfer or crosslinguistic influence (CLI), referring to the influence of an L1 on the learning of an L2, is a significant aspect of Second Language Acquisition (SLA). Much work in this area is data-driven, and consequently, large L2 corpora have been constructed for use in CLI analyses. The field of Natural Language Processing, and in particular the specific task of Grammatical Error Correction (GEC), also has corpora that can be of use in these kinds of analyses. In this paper, we take the FCE corpus, a popular dataset of English as a Second Language (ESL) learner texts used for Grammatical Error Correction model training, and use it to analyse the relationship between the distributions of errors and the first languages of the ESL learners. We carry out a detailed analysis of three error types, and demonstrate that the errors made by ESL learners have a statistically significant relationship with linguistic characteristics of their first languages, suggesting the existence of both positive and negative transfer. The analysis aligns with results from the SLA literature, and validates the use of GEC corpora for use in CLI analysis.