@inproceedings{gupta-etal-2021-task,
    title = "Task-Specific Pre-Training and Cross Lingual Transfer for Sentiment Analysis in {D}ravidian Code-Switched Languages",
    author = "Gupta, Akshat  and
      Rallabandi, Sai Krishna  and
      Black, Alan W",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Kumar M, Anand  and
      Krishnamurthy, Parameswari  and
      Sherly, Elizabeth",
    booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
    month = apr,
    year = "2021",
    address = "Kyiv",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2021.dravidianlangtech-1.9/",
    pages = "73--79",
    abstract = "Sentiment analysis in Code-Mixed languages has garnered a lot of attention in recent years. It is an important task for social media monitoring and has many applications, as a large chunk of social media data is Code-Mixed. In this paper, we work on the problem of sentiment analysis for Dravidian Code-Switched languages - Tamil-Engish and Malayalam-English, using three different BERT based models. We leverage task-specific pre-training and cross-lingual transfer to improve on previously reported results, with significant improvement for the Tamil-Engish dataset. We also present a multilingual sentiment classification model that has competitive performance on both Tamil-English and Malayalam-English datasets."
}