Abstract
Causal events play a crucial role in explaining the intricate relationships between the causes and effects of events. However, comprehending causal events within discourse, text, or speech poses significant semantic challenges. We propose a contrastive learning-based method in this submission to the Causal News Corpus - Event Causality Shared Task 2023, with a specific focus on SubTask1 centered on causal event classification. In our approach we pre-train our base model using Supervised Contrastive (SuperCon) learning. Subsequently, we fine-tune the pre-trained model for the specific task of causal event classification. Our experimentation demonstrates the effectiveness of our method, achieving a competitive performance, and securing the 2nd position on the leaderboard with an F1-Score of 84.36.- Anthology ID:
- 2023.case-1.8
- Volume:
- Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text
- Month:
- sEPTEMBER
- Year:
- 2023
- Address:
- Varna, Bulgaria
- Editors:
- Ali Hürriyetoğlu, Hristo Tanev, Vanni Zavarella, Reyyan Yeniterzi, Erdem Yörük, Milena Slavcheva
- Venues:
- CASE | WS
- SIG:
- Publisher:
- INCOMA Ltd., Shoumen, Bulgaria
- Note:
- Pages:
- 60–65
- Language:
- URL:
- https://aclanthology.org/2023.case-1.8
- DOI:
- Cite (ACL):
- Rajat Patel. 2023. InterosML@Causal News Corpus 2023: Understanding Causal Relationships: Supervised Contrastive Learning for Event Classification. In Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text, pages 60–65, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
- Cite (Informal):
- InterosML@Causal News Corpus 2023: Understanding Causal Relationships: Supervised Contrastive Learning for Event Classification (Patel, CASE-WS 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2023.case-1.8.pdf