LangResearchLab_NC at FinCausal 2020, Task 1: A Knowledge Induced Neural Net for Causality Detection

Raksha Agarwal, Ishaan Verma, Niladri Chatterjee

Abstract
Identifying causal relationships in a text is essential for achieving comprehensive natural language understanding. The present work proposes a combination of features derived from pre-trained BERT with linguistic features for training a supervised classifier for the task of Causality Detection. The Linguistic features help to inject knowledge about the semantic and syntactic structure of the input sentences. Experiments on the FinCausal Shared Task1 datasets indicate that the combination of Linguistic features with BERT improves overall performance for causality detection. The proposed system achieves a weighted average F1 score of 0.952 on the post-evaluation dataset.
Anthology ID:
2020.fnp-1.4
Volume:
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
FNP
SIG:
Publisher:
COLING
Note:
Pages:
33–39
Language:
URL:
https://aclanthology.org/2020.fnp-1.4
DOI:
Bibkey:
Cite (ACL):
Raksha Agarwal, Ishaan Verma, and Niladri Chatterjee. 2020. LangResearchLab_NC at FinCausal 2020, Task 1: A Knowledge Induced Neural Net for Causality Detection. In Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pages 33–39, Barcelona, Spain (Online). COLING.
Cite (Informal):
LangResearchLab_NC at FinCausal 2020, Task 1: A Knowledge Induced Neural Net for Causality Detection (Agarwal et al., FNP 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2020.fnp-1.4.pdf
PDF Search