iLab at FinCausal 2022: Enhancing Causality Detection with an External Cause-Effect Knowledge Graph
Ziwei Xu, Rungsiman Nararatwong, Natthawut Kertkeidkachorn, Ryutaro Ichise
Abstract
The application of span detection grows fast along with the increasing need of understanding the causes and effects of events, especially in the finance domain. However, once the syntactic clues are absent in the text, the model tends to reverse the cause and effect spans. To solve this problem, we introduce graph construction techniques to inject cause-effect graph knowledge for graph embedding. The graph features combining with BERT embedding, then are used to predict the cause effect spans. The results show our proposed graph builder method outperforms the other methods w/wo external knowledge.- Anthology ID:
- 2022.fnp-1.21
- Volume:
- Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Mahmoud El-Haj, Paul Rayson, Nadhem Zmandar
- Venue:
- FNP
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 124–127
- Language:
- URL:
- https://aclanthology.org/2022.fnp-1.21
- DOI:
- Cite (ACL):
- Ziwei Xu, Rungsiman Nararatwong, Natthawut Kertkeidkachorn, and Ryutaro Ichise. 2022. iLab at FinCausal 2022: Enhancing Causality Detection with an External Cause-Effect Knowledge Graph. In Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022, pages 124–127, Marseille, France. European Language Resources Association.
- Cite (Informal):
- iLab at FinCausal 2022: Enhancing Causality Detection with an External Cause-Effect Knowledge Graph (Xu et al., FNP 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.fnp-1.21.pdf