A Shallow Neural Network for Native Language Identification with Character N-grams
Yunita Sari, Muhammad Rifqi Fatchurrahman, Meisyarah Dwiastuti
Abstract
This paper describes the systems submitted by GadjahMada team to the Native Language Identification (NLI) Shared Task 2017. Our models used a continuous representation of character n-grams which are learned jointly with feed-forward neural network classifier. Character n-grams have been proved to be effective for style-based identification tasks including NLI. Results on the test set demonstrate that the proposed model performs very well on essay and fusion tracks by obtaining more than 0.8 on both F-macro score and accuracy.- Anthology ID:
- W17-5027
- Volume:
- Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 249–254
- Language:
- URL:
- https://aclanthology.org/W17-5027
- DOI:
- 10.18653/v1/W17-5027
- Cite (ACL):
- Yunita Sari, Muhammad Rifqi Fatchurrahman, and Meisyarah Dwiastuti. 2017. A Shallow Neural Network for Native Language Identification with Character N-grams. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 249–254, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- A Shallow Neural Network for Native Language Identification with Character N-grams (Sari et al., BEA 2017)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W17-5027.pdf