@inproceedings{saha-etal-2022-spock,
    title = "{SPOCK} at {F}in{C}ausal 2022: Causal Information Extraction Using Span-Based and Sequence Tagging Models",
    author = "Saha, Anik  and
      Ni, Jian  and
      Hassanzadeh, Oktie  and
      Gittens, Alex  and
      Srinivas, Kavitha  and
      Yener, Bulent",
    booktitle = "Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2022.fnp-1.17",
    pages = "108--111",
    abstract = "Causal information extraction is an important task in natural language processing, particularly in finance domain. In this work, we develop several information extraction models using pre-trained transformer-based language models for identifying cause and effect text spans from financial documents. We use FinCausal 2021 and 2022 data sets to train span-based and sequence tagging models. Our ensemble of sequence tagging models based on the RoBERTa-Large pre-trained language model achieves an F1 score of 94.70 with Exact Match score of 85.85 and obtains the 1st place in the FinCausal 2022 competition.",
}