Using Ensemble Methods and Sequence Tagging to DetectCausality in Financial Documents

The FinCausal 2020 shared task aims to detect causality on financial news and identify those parts of the causal sentences related to the underlying cause and effect. We apply ensemble-based and sequence tagging methods for identifying causality, and extracting causal subsequences. Our models yield promising results on both sub-tasks, with the prospect of further improvement given more time and computing resources. With respect to task 1, we achieved an F1 score of 0.9429 on the evaluation data, and a corresponding ranking of 12/14. For task 2, we were ranked 6/10, with an F1 score of 0.76 and an ExactMatch score of 0.1912.

  • Published in:
    Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FinCausal)
  • Type:
    Inproceedings
  • Authors:
    M. Pielka, R. Ramamurthy, A. Ladi, E. Brito, C. Chapman, P. Mayer, R. Sifa
  • Year:
    2020

Citation information

M. Pielka, R. Ramamurthy, A. Ladi, E. Brito, C. Chapman, P. Mayer, R. Sifa: Using Ensemble Methods and Sequence Tagging to DetectCausality in Financial Documents, Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FinCausal), 2020, https://aclanthology.org/2020.fnp-1.10, Pielka.etal.2020,