@inproceedings{kramp-etal-2024-bignli-native,
title = "{B}ig{NLI}: Native Language Identification with Big Bird Embeddings",
author = "Kramp, Sergey and
Cassani, Giovanni and
Emmery, Chris",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.212",
pages = "2375--2382",
abstract = "Native Language Identification (NLI) intends to classify an author{'}s native language based on their writing in another language. Historically, the task has heavily relied on time-consuming linguistic feature engineering, and NLI transformer models have thus far failed to offer effective, practical alternatives. The current work shows input size is a limiting factor, and that classifiers trained using Big Bird embeddings outperform linguistic feature engineering models (for which we reproduce previous work) by a large margin on the Reddit-L2 dataset. Additionally, we provide further insight into input length dependencies, show consistent out-of-sample (Europe subreddit) and out-of-domain (TOEFL-11) performance, and qualitatively analyze the embedding space. Given the effectiveness and computational efficiency of this method, we believe it offers a promising avenue for future NLI work.",
}
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<abstract>Native Language Identification (NLI) intends to classify an author’s native language based on their writing in another language. Historically, the task has heavily relied on time-consuming linguistic feature engineering, and NLI transformer models have thus far failed to offer effective, practical alternatives. The current work shows input size is a limiting factor, and that classifiers trained using Big Bird embeddings outperform linguistic feature engineering models (for which we reproduce previous work) by a large margin on the Reddit-L2 dataset. Additionally, we provide further insight into input length dependencies, show consistent out-of-sample (Europe subreddit) and out-of-domain (TOEFL-11) performance, and qualitatively analyze the embedding space. Given the effectiveness and computational efficiency of this method, we believe it offers a promising avenue for future NLI work.</abstract>
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%0 Conference Proceedings
%T BigNLI: Native Language Identification with Big Bird Embeddings
%A Kramp, Sergey
%A Cassani, Giovanni
%A Emmery, Chris
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F kramp-etal-2024-bignli-native
%X Native Language Identification (NLI) intends to classify an author’s native language based on their writing in another language. Historically, the task has heavily relied on time-consuming linguistic feature engineering, and NLI transformer models have thus far failed to offer effective, practical alternatives. The current work shows input size is a limiting factor, and that classifiers trained using Big Bird embeddings outperform linguistic feature engineering models (for which we reproduce previous work) by a large margin on the Reddit-L2 dataset. Additionally, we provide further insight into input length dependencies, show consistent out-of-sample (Europe subreddit) and out-of-domain (TOEFL-11) performance, and qualitatively analyze the embedding space. Given the effectiveness and computational efficiency of this method, we believe it offers a promising avenue for future NLI work.
%U https://aclanthology.org/2024.lrec-main.212
%P 2375-2382
Markdown (Informal)
[BigNLI: Native Language Identification with Big Bird Embeddings](https://aclanthology.org/2024.lrec-main.212) (Kramp et al., LREC-COLING 2024)
ACL