@inproceedings{hettiarachchi-etal-2021-daai,
title = "{DAAI} at {CASE} 2021 Task 1: Transformer-based Multilingual Socio-political and Crisis Event Detection",
author = "Hettiarachchi, Hansi and
Adedoyin-Olowe, Mariam and
Bhogal, Jagdev and
Gaber, Mohamed Medhat",
booktitle = "Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.case-1.16",
doi = "10.18653/v1/2021.case-1.16",
pages = "120--130",
abstract = "Automatic socio-political and crisis event detection has been a challenge for natural language processing as well as social and political science communities, due to the diversity and nuance in such events and high accuracy requirements. In this paper, we propose an approach which can handle both document and cross-sentence level event detection in a multilingual setting using pretrained transformer models. Our approach became the winning solution in document level predictions and secured the 3rd place in cross-sentence level predictions for the English language. We could also achieve competitive results for other languages to prove the effectiveness and universality of our approach.",
}
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%0 Conference Proceedings
%T DAAI at CASE 2021 Task 1: Transformer-based Multilingual Socio-political and Crisis Event Detection
%A Hettiarachchi, Hansi
%A Adedoyin-Olowe, Mariam
%A Bhogal, Jagdev
%A Gaber, Mohamed Medhat
%S Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)
%D 2021
%8 aug
%I Association for Computational Linguistics
%C Online
%F hettiarachchi-etal-2021-daai
%X Automatic socio-political and crisis event detection has been a challenge for natural language processing as well as social and political science communities, due to the diversity and nuance in such events and high accuracy requirements. In this paper, we propose an approach which can handle both document and cross-sentence level event detection in a multilingual setting using pretrained transformer models. Our approach became the winning solution in document level predictions and secured the 3rd place in cross-sentence level predictions for the English language. We could also achieve competitive results for other languages to prove the effectiveness and universality of our approach.
%R 10.18653/v1/2021.case-1.16
%U https://aclanthology.org/2021.case-1.16
%U https://doi.org/10.18653/v1/2021.case-1.16
%P 120-130
Markdown (Informal)
[DAAI at CASE 2021 Task 1: Transformer-based Multilingual Socio-political and Crisis Event Detection](https://aclanthology.org/2021.case-1.16) (Hettiarachchi et al., CASE 2021)
ACL