@inproceedings{panda-levitan-2021-hunterspeechlab,
    title = "{H}unter{S}peech{L}ab at {G}erm{E}val 2021: Does Your Comment Claim A Fact? Contextualized Embeddings for {G}erman Fact-Claiming Comment Classification",
    author = "Panda, Subhadarshi  and
      Levitan, Sarah Ita",
    editor = "Risch, Julian  and
      Stoll, Anke  and
      Wilms, Lena  and
      Wiegand, Michael",
    booktitle = "Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments",
    month = sep,
    year = "2021",
    address = "Duesseldorf, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.germeval-1.15/",
    pages = "100--104",
    abstract = "In this paper we investigate the efficacy of using contextual embeddings from multilingual BERT and German BERT in identifying fact-claiming comments in German on social media. Additionally, we examine the impact of formulating the classification problem as a multi-task learning problem, where the model identifies toxicity and engagement of the comment in addition to identifying whether it is fact-claiming. We provide a thorough comparison of the two BERT based models compared with a logistic regression baseline and show that German BERT features trained using a multi-task objective achieves the best F1 score on the test set. This work was done as part of a submission to GermEval 2021 shared task on the identification of fact-claiming comments."
}