@inproceedings{suri-etal-2022-nsut,
    title = "{NSUT}-{NLP} at {CASE} 2022 Task 1: Multilingual Protest Event Detection using Transformer-based Models",
    author = "Suri, Manan  and
      Chopra, Krish  and
      Arora, Adwita",
    editor = {H{\"u}rriyeto{\u{g}}lu, Ali  and
      Tanev, Hristo  and
      Zavarella, Vanni  and
      Y{\"o}r{\"u}k, Erdem},
    booktitle = "Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.case-1.23/",
    doi = "10.18653/v1/2022.case-1.23",
    pages = "161--168",
    abstract = "Event detection, specifically in the socio-political domain, has posed a long-standing challenge to researchers in the NLP domain. Therefore, the creation of automated techniques that perform classification of the large amounts of accessible data on the Internet becomes imperative. This paper is a summary of the efforts we made in participating in Task 1 of CASE 2022. We use state-of-art multilingual BERT (mBERT) with further fine-tuning to perform document classification in English, Portuguese, Spanish, Urdu, Hindi, Turkish and Mandarin. In the document classification subtask, we were able to achieve F1 scores of 0.8062, 0.6445, 0.7302, 0.5671, 0.6555, 0.7545 and 0.6702 in English, Spanish, Portuguese, Hindi, Urdu, Mandarin and Turkish respectively achieving a rank of 5 in English and 7 on the remaining language tasks."
}