@inproceedings{ozates-cetinoglu-2021-language,
    title = "A Language-aware Approach to Code-switched Morphological Tagging",
    author = {{\"O}zate{\c{s}}, {\c{S}}aziye Bet{\"u}l  and
      {\c{C}}etino{\u{g}}lu, {\"O}zlem},
    editor = "Solorio, Thamar  and
      Chen, Shuguang  and
      Black, Alan W.  and
      Diab, Mona  and
      Sitaram, Sunayana  and
      Soto, Victor  and
      Yilmaz, Emre  and
      Srinivasan, Anirudh",
    booktitle = "Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.calcs-1.10/",
    doi = "10.18653/v1/2021.calcs-1.10",
    pages = "72--83",
    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."
}