Abstract
Native language identification (NLI) – identifying the native language (L1) of a person based on his/her writing in the second language (L2) – is useful for a variety of purposes, including marketing, security, and educational applications. From a traditional machine learning perspective,NLI is usually framed as a multi-class classification task, where numerous designed features are combined in order to achieve the state-of-the-art results. We introduce a deep generative language modelling (LM) approach to NLI, which consists in fine-tuning a GPT-2 model separately on texts written by the authors with the same L1, and assigning a label to an unseen text based on the minimum LM loss with respect to one of these fine-tuned GPT-2 models. Our method outperforms traditional machine learning approaches and currently achieves the best results on the benchmark NLI datasets.- Anthology ID:
- 2020.coling-main.159
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 1778–1783
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.159
- DOI:
- 10.18653/v1/2020.coling-main.159
- Cite (ACL):
- Ehsan Lotfi, Ilia Markov, and Walter Daelemans. 2020. A Deep Generative Approach to Native Language Identification. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1778–1783, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- A Deep Generative Approach to Native Language Identification (Lotfi et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.159.pdf