@inproceedings{he-etal-2022-zhegu,
    title = "Zhegu@{SMM}4{H}-2022: The Pre-training Tweet {\&} Claim Matching Makes Your Prediction Better",
    author = "He, Pan  and
      YuZe, Chen  and
      Zhang, Yanru",
    editor = "Gonzalez-Hernandez, Graciela  and
      Weissenbacher, Davy",
    booktitle = "Proceedings of the Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.smm4h-1.11/",
    pages = "38--41",
    abstract = "SMM4H-2022 (CITATION) Task 2 is to detect whether containing premise in the tweets of users about COVID-19 on the social medias or their stances for the claims. In this paper, we propose \textbf{T}weet \textbf{C}laim \textbf{M}atching (\textbf{TCM}), which is a new pre-training task constructed by the tweets and claims similarly to Next Sentence Prediction (NSP). We first continue to pre-train the standard pre-trained language models on the labelled dataset and then fine-tune them for obtaining better performance. Compared with the solid baseline (CITATION), we achieve the absolute improvement of 7.9{\%} in Task 2a and obtain the SOTA results."
}