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.- Anthology ID:
- 2024.lrec-main.212
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 2375–2382
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.212
- DOI:
- Cite (ACL):
- Sergey Kramp, Giovanni Cassani, and Chris Emmery. 2024. BigNLI: Native Language Identification with Big Bird Embeddings. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 2375–2382, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- BigNLI: Native Language Identification with Big Bird Embeddings (Kramp et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.212.pdf