@inproceedings{soto-hirschberg-2018-joint,
    title = "Joint Part-of-Speech and Language {ID} Tagging for Code-Switched Data",
    author = "Soto, Victor  and
      Hirschberg, Julia",
    editor = "Aguilar, Gustavo  and
      AlGhamdi, Fahad  and
      Soto, Victor  and
      Solorio, Thamar  and
      Diab, Mona  and
      Hirschberg, Julia",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-3201",
    doi = "10.18653/v1/W18-3201",
    pages = "1--10",
    abstract = "Code-switching is the fluent alternation between two or more languages in conversation between bilinguals. Large populations of speakers code-switch during communication, but little effort has been made to develop tools for code-switching, including part-of-speech taggers. In this paper, we propose an approach to POS tagging of code-switched English-Spanish data based on recurrent neural networks. We test our model on known monolingual benchmarks to demonstrate that our neural POS tagging model is on par with state-of-the-art methods. We next test our code-switched methods on the Miami Bangor corpus of English Spanish conversation, focusing on two types of experiments: POS tagging alone, for which we achieve 96.34{\%} accuracy, and joint part-of-speech and language ID tagging, which achieves similar POS tagging accuracy (96.39{\%}) and very high language ID accuracy (98.78{\%}). Finally, we show that our proposed models outperform other state-of-the-art code-switched taggers.",
}