Abstract
Morphological tagging of code-switching (CS) data becomes more challenging especially when language pairs composing the CS data have different morphological representations. In this paper, we explore a number of ways of implementing a language-aware morphological tagging method and present our approach for integrating language IDs into a transformer-based framework for CS morphological tagging. We perform our set of experiments on the Turkish-German SAGT Treebank. Experimental results show that including language IDs to the learning model significantly improves accuracy over other approaches.- Anthology ID:
- 2021.calcs-1.10
- Volume:
- Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Thamar Solorio, Shuguang Chen, Alan W. Black, Mona Diab, Sunayana Sitaram, Victor Soto, Emre Yilmaz, Anirudh Srinivasan
- Venue:
- CALCS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 72–83
- Language:
- URL:
- https://aclanthology.org/2021.calcs-1.10
- DOI:
- 10.18653/v1/2021.calcs-1.10
- Cite (ACL):
- Şaziye Betül Özateş and Özlem Çetinoğlu. 2021. A Language-aware Approach to Code-switched Morphological Tagging. In Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching, pages 72–83, Online. Association for Computational Linguistics.
- Cite (Informal):
- A Language-aware Approach to Code-switched Morphological Tagging (Özateş & Çetinoğlu, CALCS 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.calcs-1.10.pdf
- Data
- Universal Dependencies