Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks
Şaziye Betül Özateş, Arzucan Özgür, Tunga Güngör, Özlem Çetinoğlu · Findings of the Association for Computational Linguistics: NAACL 2022 · 2022
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-Rbased model with significantly less computational and space 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.