SPARTA at CASE 2021 Task 1: Evaluating Different Techniques to Improve Event Extraction

Arthur Müller, Andreas Dafnos


Abstract
We participated in the Shared Task 1 at CASE 2021, Subtask 4 on protest event extraction from news articles and examined different techniques aimed at improving the performance of the winning system from the last competition round. We evaluated in-domain pre-training, task-specific pre-fine-tuning, alternative loss function, translation of the English training dataset into other target languages (i.e., Portuguese, Spanish, and Hindi) for the token classification task, and a simple data augmentation technique by random sentence reordering. This paper summarizes the results, showing that random sentence reordering leads to a consistent improvement of the model performance.
Anthology ID:
2022.case-1.27
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:
189–194
Language:
URL:
https://aclanthology.org/2022.case-1.27
DOI:
10.18653/v1/2022.case-1.27
Bibkey:
Cite (ACL):
Arthur Müller and Andreas Dafnos. 2022. SPARTA at CASE 2021 Task 1: Evaluating Different Techniques to Improve Event Extraction. In Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE), pages 189–194, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
SPARTA at CASE 2021 Task 1: Evaluating Different Techniques to Improve Event Extraction (Müller & Dafnos, CASE 2022)
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PDF:
https://aclanthology.org/2022.case-1.27.pdf