@inproceedings{nguyen-mitra-2022-noisyannot,
title = "{N}oisy{A}nnot@ Causal News Corpus 2022: Causality Detection using Multiple Annotation Decisions",
author = "Nguyen, Quynh Anh and
Mitra, Arka",
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-1.11/",
doi = "10.18653/v1/2022.case-1.11",
pages = "79--84",
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."
}
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%0 Conference Proceedings
%T NoisyAnnot@ Causal News Corpus 2022: Causality Detection using Multiple Annotation Decisions
%A Nguyen, Quynh Anh
%A Mitra, Arka
%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 nguyen-mitra-2022-noisyannot
%X 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.
%R 10.18653/v1/2022.case-1.11
%U https://aclanthology.org/2022.case-1.11/
%U https://doi.org/10.18653/v1/2022.case-1.11
%P 79-84
Markdown (Informal)
[NoisyAnnot@ Causal News Corpus 2022: Causality Detection using Multiple Annotation Decisions](https://aclanthology.org/2022.case-1.11/) (Nguyen & Mitra, CASE 2022)
ACL