Abstract
The paper describes the work that has been submitted to the 5th workshop on Challenges and Applications of Automated Extraction of socio-political events from text (CASE 2022). The work is associated with Subtask 1 of Shared Task 3 that aims to detect causality in protest news corpus. The authors used different large language models with customized cross-entropy loss functions that exploit annotation information. The experiments showed that bert-based-uncased with refined cross-entropy outperformed the others, achieving a F1 score of 0.8501 on the Causal News Corpus dataset.- Anthology ID:
- 2022.case-1.11
- 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:
- 79–84
- Language:
- URL:
- https://aclanthology.org/2022.case-1.11
- DOI:
- 10.18653/v1/2022.case-1.11
- Cite (ACL):
- Quynh Anh Nguyen and Arka Mitra. 2022. NoisyAnnot@ Causal News Corpus 2022: Causality Detection using Multiple Annotation Decisions. In Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE), pages 79–84, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- NoisyAnnot@ Causal News Corpus 2022: Causality Detection using Multiple Annotation Decisions (Nguyen & Mitra, CASE 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.case-1.11.pdf