@inproceedings{muller-dafnos-2022-sparta,
title = "{SPARTA} at {CASE} 2021 Task 1: Evaluating Different Techniques to Improve Event Extraction",
author = {M{\"u}ller, Arthur and
Dafnos, Andreas},
editor = {H{\"u}rriyeto{\u{g}}lu, Ali and
Tanev, Hristo and
Zavarella, Vanni and
Y{\"o}r{\"u}k, Erdem},
booktitle = "Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.case-1.27",
doi = "10.18653/v1/2022.case-1.27",
pages = "189--194",
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.",
}
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%0 Conference Proceedings
%T SPARTA at CASE 2021 Task 1: Evaluating Different Techniques to Improve Event Extraction
%A Müller, Arthur
%A Dafnos, Andreas
%Y Hürriyetoğlu, Ali
%Y Tanev, Hristo
%Y Zavarella, Vanni
%Y Yörük, Erdem
%S Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F muller-dafnos-2022-sparta
%X 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.
%R 10.18653/v1/2022.case-1.27
%U https://aclanthology.org/2022.case-1.27
%U https://doi.org/10.18653/v1/2022.case-1.27
%P 189-194
Markdown (Informal)
[SPARTA at CASE 2021 Task 1: Evaluating Different Techniques to Improve Event Extraction](https://aclanthology.org/2022.case-1.27) (Müller & Dafnos, CASE 2022)
ACL