A Language-aware Approach to Code-switched Morphological Tagging

Şaziye Betül Özateş, Özlem Çetinoğlu


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
Venues:
CALCS | NAACL
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
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.calcs-1.10.pdf
Data
Universal Dependencies