@inproceedings{ramanarayanan-pugh-2018-automatic,
    title = "Automatic Token and Turn Level Language Identification for Code-Switched Text Dialog: An Analysis Across Language Pairs and Corpora",
    author = "Ramanarayanan, Vikram  and
      Pugh, Robert",
    editor = "Komatani, Kazunori  and
      Litman, Diane  and
      Yu, Kai  and
      Papangelis, Alex  and
      Cavedon, Lawrence  and
      Nakano, Mikio",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/W18-5009/",
    doi = "10.18653/v1/W18-5009",
    pages = "80--88",
    abstract = "We examine the efficacy of various feature{--}learner combinations for language identification in different types of text-based code-switched interactions {--} human-human dialog, human-machine dialog as well as monolog {--} at both the token and turn levels. In order to examine the generalization of such methods across language pairs and datasets, we analyze 10 different datasets of code-switched text. We extract a variety of character- and word-based text features and pass them into multiple learners, including conditional random fields, logistic regressors and recurrent neural networks. We further examine the efficacy of novel character-level embedding and GloVe features in improving performance and observe that our best-performing text system significantly outperforms a majority vote baseline across language pairs and datasets."
}