Papers with Native Language Identification

5 papers
Using Classifier Features to Determine Language Transfer on Morphemes (N18-4)

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Challenge: Using native English data, we identify an English learner’s native language background based solely on the learner's English writing samples.
Approach: They perform a Native Language Identification task where they identify an English learner’s native language background based only on the learner's English writing samples.
Outcome: The proposed task is connected to a position in second language acquisition research that holds all learners acquire English grammatical morphemes in the same order, regardless of native language background.
Multilingual Native Language Identification with Large Language Models (2025.naacl-srw)

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Challenge: Native Language Identification (NLI) is the task of automatically identifying the native language (L1) of individuals based on their second language production.
Approach: They evaluated the performance of several LLMs on non-English NLI corpora compared to traditional statistical machine learning models and language-specific BERT-based models.
Outcome: The proposed models outperform statistical models and language-specific BERT-based models on English, Italian, Norwegian, and Portuguese.
Native Language Identification in Texts: A Survey (2024.naacl-long)

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Challenge: Native language identification is the task of automatically identifying an author’s native language (L1) based on their second language production.
Approach: They present a survey of native language identification applied to texts . authors describe several text representations and computational techniques used in the task .
Outcome: The proposed task has been widely studied for both text and speech, particularly for L2 English due to the availability of suitable corpora.
BigNLI: Native Language Identification with Big Bird Embeddings (2024.lrec-main)

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Challenge: Native Language Identification (NLI) is a task that relies on time-consuming linguistic feature engineering and current transformer models are limited by input size.
Approach: They propose to train a logistic regression classifier which only uses Big Bird embeddings to overcome this limitation.
Outcome: The proposed method outperforms linguistic feature engineering models on the Reddit-L2 dataset and shows consistent out-of-sample and out-off-domain performance.
Tracing L1 Interference in English Learner Writing: A Longitudinal Corpus with Error Annotations (2025.emnlp-main)

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Challenge: high-quality learner corpora are rarely available for studies of second language acquisition and language transfer.
Approach: They propose to curate a corpus of adult learners with longitudinal data that includes 15 different L1s.
Outcome: The proposed corpus contains 687 texts written by adult learners in the USA . authors show that the corpus can be used to explore language learning trajectories over time.

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