Tracing L1 Interference in English Learner Writing: A Longitudinal Corpus with Error Annotations

ACL ARR 2025 May Submission4484 Authors

Abstract:

The availability of suitable learner corpora is crucial for studying second language acquisition (SLA) and language transfer. However, curating such corpora is challenging, as high-quality learner data is rarely publicly available. As a result, only a few learner corpora, such as ICLE and TOEFL-11, are accessible to the research community.

To address this gap, we present Anonymous, a novel English learner corpus with longitudinal data. The corpus consists of 687 texts written by adult learners taking English as a second language courses in the USA. These learners are either preparing for university admission or enhancing their language proficiency while beginning their university studies. Unlike most learner corpora, Anonymous includes longitudinal data, allowing researchers to explore language learning trajectories over time. The corpus features contributions from speakers of 15 different L1s.

We demonstrate the utility of Anonymous through two case studies at the intersection of SLA and Computational Linguistics: (1) Native Language Identification (NLI), and (2) a quantitative and qualitative analysis of linguistic features influenced by L1 using large language models

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: linguistic theories, computational psycholinguistics, GEC, educational applications, NLI

Contribution Types: Data resources

Languages Studied: English, Chinese, Arabic

Submission Number: 4484