IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach
Sergio Burdisso, Juan Pablo Zuluaga-gomez, Esau Villatoro-tello, Martin Fajcik, Muskaan Singh, Pavel Smrz, Petr Motlicek
Abstract
In this paper, we describe our participation in the subtask 1 of CASE-2022, Event Causality Identification with Casual News Corpus. We address the Causal Relation Identification (CRI) task by exploiting a set of simple yet complementary techniques for fine-tuning language models (LMs) on a few annotated examples (i.e., a few-shot configuration).We follow a prompt-based prediction approach for fine-tuning LMs in which the CRI task is treated as a masked language modeling problem (MLM). This approach allows LMs natively pre-trained on MLM tasks to directly generate textual responses to CRI-specific prompts. We compare the performance of this method against ensemble techniques trained on the entire dataset. Our best-performing submission was fine-tuned with only 256 instances per class, 15.7% of the all available data, and yet obtained the second-best precision (0.82), third-best accuracy (0.82), and an F1-score (0.85) very close to what was reported by the winner team (0.86).- Anthology ID:
- 2022.case-1.9
- 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:
- 61–69
- Language:
- URL:
- https://aclanthology.org/2022.case-1.9
- DOI:
- 10.18653/v1/2022.case-1.9
- Cite (ACL):
- Sergio Burdisso, Juan Pablo Zuluaga-gomez, Esau Villatoro-tello, Martin Fajcik, Muskaan Singh, Pavel Smrz, and Petr Motlicek. 2022. IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach. In Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE), pages 61–69, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach (Burdisso et al., CASE 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.case-1.9.pdf