@inproceedings{imoto-ito-2020-jdd,
    title = "{JDD} @ {F}in{C}ausal 2020, Task 2: Financial Document Causality Detection",
    author = "Imoto, Toshiya  and
      Ito, Tomoki",
    editor = "El-Haj, Dr Mahmoud  and
      Athanasakou, Dr Vasiliki  and
      Ferradans, Dr Sira  and
      Salzedo, Dr Catherine  and
      Elhag, Dr Ans  and
      Bouamor, Dr Houda  and
      Litvak, Dr Marina  and
      Rayson, Dr Paul  and
      Giannakopoulos, Dr George  and
      Pittaras, Nikiforos",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.fnp-1.7/",
    pages = "50--54",
    abstract = "This paper describes the approach we built for the Financial Document Causality Detection Shared Task (FinCausal-2020) Task 2: Cause and Effect Detection. Our approach is based on a multi-class classifier using BiLSTM with Graph Convolutional Neural Network (GCN) trained by minimizing the binary cross entropy loss. In our approach, we have not used any extra data source apart from combining the trial and practice dataset. We achieve weighted F1 score to 75.61 percent and are ranked at 7-th place."
}