@inproceedings{lee-etal-2022-mnlp,
    title = "{MNLP} at {F}in{C}ausal2022: Nested {NER} with a Generative Model",
    author = {Lee, Jooyeon  and
      Pham, Luan Huy  and
      Uzuner, {\"O}zlem},
    editor = "El-Haj, Mahmoud  and
      Rayson, Paul  and
      Zmandar, Nadhem",
    booktitle = "Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.fnp-1.24/",
    pages = "135--138",
    abstract = "This paper describes work performed for the FinCasual 2022 Shared Task {\textquotedblleft}Financial Document Causality Detection{\textquotedblright} (FinCausal 2022). As the name implies, the task involves extraction of casual and consequential elements from financial text. Our approach focuses employing Nested NER using the Text-to-Text Transformer (T5) generative transformer models while applying different combinations of datasets and tagging methods. Our system reports accuracy of 79{\%} in Exact Match comparison and F-measure score of 92{\%} token level measurement."
}