@inproceedings{hettiarachchi-ranasinghe-2020-infominer,
    title = "{I}nfo{M}iner at {WNUT}-2020 Task 2: Transformer-based Covid-19 Informative Tweet Extraction",
    author = "Hettiarachchi, Hansi  and
      Ranasinghe, Tharindu",
    editor = "Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim  and
      Rahimi, Afshin",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.wnut-1.49/",
    doi = "10.18653/v1/2020.wnut-1.49",
    pages = "359--365",
    abstract = "Identifying informative tweets is an important step when building information extraction systems based on social media. WNUT-2020 Task 2 was organised to recognise informative tweets from noise tweets. In this paper, we present our approach to tackle the task objective using transformers. Overall, our approach achieves 10th place in the final rankings scoring 0.9004 F1 score for the test set."
}