Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks
Şaziye Betül Özateş, Arzucan Özgür, Tunga Gungor, Özlem Çetinoğlu
Abstract
Code-switching dependency parsing stands as a challenging task due to both the scarcity of necessary resources and the structural difficulties embedded in code-switched languages. In this study, we introduce novel sequence labeling models to be used as auxiliary tasks for dependency parsing of code-switched text in a semi-supervised scheme. We show that using auxiliary tasks enhances the performance of an LSTM-based dependency parsing model and leads to better results compared to an XLM-R-based model with significantly less computational and time complexity. As the first study that focuses on multiple code-switching language pairs for dependency parsing, we acquire state-of-the-art scores on all of the studied languages. Our best models outperform the previous work by 7.4 LAS points on average.
BibTeX
@inproceedings{ozates-etal-2022-improving,
title = "Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks",
author = {{\"O}zate{\c{s}}, {\c{S}}aziye Bet{\"u}l and
{\"O}zg{\"u}r, Arzucan and
Gungor, Tunga and
{\c{C}}etino{\u{g}}lu, {\"O}zlem},
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.87/",
doi = "10.18653/v1/2022.findings-naacl.87",
pages = "1159--1171"
}