@inproceedings{deka-etal-2022-bert,
    title = "{BERT}-based Language Identification in Code-Mix {K}annada-{E}nglish Text at the {C}o{LI}-Kanglish Shared Task@{ICON} 2022",
    author = "Deka, Pritam  and
      Jyoti Kalita, Nayan  and
      Kumar Sarma, Shikhar",
    editor = "Chakravarthi, Bharathi Raja  and
      Murugappan, Abirami  and
      Chinnappa, Dhivya  and
      Hane, Adeep  and
      Kumeresan, Prasanna Kumar  and
      Ponnusamy, Rahul",
    booktitle = "Proceedings of the 19th International Conference on Natural Language Processing (ICON): Shared Task on Word Level Language Identification in Code-mixed Kannada-English Texts",
    month = dec,
    year = "2022",
    address = "IIIT Delhi, New Delhi, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.icon-wlli.3/",
    pages = "12--17",
    abstract = "Language identification has recently gained research interest in code-mixed languages due to the extensive use of social media among people. People who speak multiple languages tend to use code-mixed languages when communicating with each other. It has become necessary to identify the languages in such code-mixed environment to detect hate speeches, fake news, misinformation or disinformation and for tasks such as sentiment analysis. In this work, we have proposed a BERT-based approach for language identification in the CoLI-Kanglish shared task at ICON 2022. Our approach achieved 86{\%} weighted average F-1 score and a macro average F-1 score of 57{\%} in the test set."
}