Abstract
Causality detection and identification is centered on identifying semantic and cognitive connections in a sentence. In this paper, we describe the effort of team LTRC for Causal News Corpus - Event Causality Shared Task 2022 at the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2022). The shared task consisted of two subtasks: 1) identifying if a sentence contains a causality relation, and 2) identifying spans of text that correspond to cause, effect and signals. We fine-tuned transformer-based models with adapters for both subtasks. Our best-performing models obtained a binary F1 score of 0.853 on held-out data for subtask 1 and a macro F1 score of 0.032 on held-out data for subtask 2. Our approach is ranked third in subtask 1 and fourth in subtask 2. The paper describes our experiments, solutions, and analysis in detail.- Anthology ID:
- 2022.case-1.7
- Volume:
- Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Ali Hürriyetoğlu, Hristo Tanev, Vanni Zavarella, Erdem Yörük
- Venue:
- CASE
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 50–55
- Language:
- URL:
- https://aclanthology.org/2022.case-1.7
- DOI:
- 10.18653/v1/2022.case-1.7
- Cite (ACL):
- Hiranmai Sri Adibhatla and Manish Shrivastava. 2022. LTRC @ Causal News Corpus 2022: Extracting and Identifying Causal Elements using Adapters. In Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE), pages 50–55, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- LTRC @ Causal News Corpus 2022: Extracting and Identifying Causal Elements using Adapters (Sri Adibhatla & Shrivastava, CASE 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.case-1.7.pdf