@inproceedings{srinivasan-etal-2020-code,
    title = "Code-mixed parse trees and how to find them",
    author = "Srinivasan, Anirudh  and
      Dandapat, Sandipan  and
      Choudhury, Monojit",
    editor = "Solorio, Thamar  and
      Choudhury, Monojit  and
      Bali, Kalika  and
      Sitaram, Sunayana  and
      Das, Amitava  and
      Diab, Mona",
    booktitle = "Proceedings of the 4th Workshop on Computational Approaches to Code Switching",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.calcs-1.8/",
    pages = "57--64",
    language = "eng",
    ISBN = "979-10-95546-66-5",
    abstract = "In this paper, we explore the methods of obtaining parse trees of code-mixed sentences and analyse the obtained trees. Existing work has shown that linguistic theories can be used to generate code-mixed sentences from a set of parallel sentences. We build upon this work, using one of these theories, the Equivalence-Constraint theory to obtain the parse trees of synthetically generated code-mixed sentences and evaluate them with a neural constituency parser. We highlight the lack of a dataset non-synthetic code-mixed constituency parse trees and how it makes our evaluation difficult. To complete our evaluation, we convert a code-mixed dependency parse tree set into {\textquotedblleft}pseudo constituency trees{\textquotedblright} and find that a parser trained on synthetically generated trees is able to decently parse these as well."
}