Abstract
With the growth of the social web, user-generated text data has reached unprecedented sizes. Non-canonical text normalization provides a way to exploit this as a practical source of training data for language processing systems. The state of the art in Turkish text normalization is composed of a token level pipeline of modules, heavily dependent on external linguistic resources and manually defined rules. Instead, we propose a fully automated, context-aware machine translation approach with fewer stages of processing. Experiments with various implementations of our approach show that we are able to surpass the current best-performing system by a large margin.- Anthology ID:
- P19-2037
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Fernando Alva-Manchego, Eunsol Choi, Daniel Khashabi
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 267–272
- Language:
- URL:
- https://aclanthology.org/P19-2037
- DOI:
- 10.18653/v1/P19-2037
- Cite (ACL):
- Talha Çolakoğlu, Umut Sulubacak, and Ahmet Cüneyd Tantuğ. 2019. Normalizing Non-canonical Turkish Texts Using Machine Translation Approaches. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 267–272, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Normalizing Non-canonical Turkish Texts Using Machine Translation Approaches (Çolakoğlu et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/P19-2037.pdf
- Data
- OpenSubtitles