@inproceedings{chen-etal-2022-1cademy,
    title = "1{C}ademy @ Causal News Corpus 2022: Enhance Causal Span Detection via Beam-Search-based Position Selector",
    author = "Chen, Xingran  and
      Zhang, Ge  and
      Nik, Adam  and
      Li, Mingyu  and
      Fu, Jie",
    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-1.14/",
    doi = "10.18653/v1/2022.case-1.14",
    pages = "100--105",
    abstract = "In this paper, we present our approach and empirical observations for Cause-Effect Signal Span Detection{---}Subtask 2 of Shared task 3 at CASE 2022. The shared task aims to extract the cause, effect, and signal spans from a given causal sentence. We model the task as a reading comprehension (RC) problem and apply a token-level RC-based span prediction paradigm to the task as the baseline. We explore different training objectives to fine-tune the model, as well as data augmentation (DA) tricks based on the language model (LM) for performance improvement. Additionally, we propose an efficient beam-search post-processing strategy to due with the drawbacks of span detection to obtain a further performance gain. Our approach achieves an average $F_1$ score of 54.15 and ranks \textbf{ $1ˆ{st}$ } in the CASE competition. Our code is available at \url{https://github.com/Gzhang-umich/1CademyTeamOfCASE}."
}