@inproceedings{yamani-etal-2022-event,
title = "Event-Based Knowledge {MLM} for {A}rabic Event Detection",
author = "Yamani, Asma Z and
Alsulami, Amjad K and
Al-Zaidy, Rabeah A",
editor = "Bouamor, Houda and
Al-Khalifa, Hend and
Darwish, Kareem and
Rambow, Owen and
Bougares, Fethi and
Abdelali, Ahmed and
Tomeh, Nadi and
Khalifa, Salam and
Zaghouani, Wajdi",
booktitle = "Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wanlp-1.25",
doi = "10.18653/v1/2022.wanlp-1.25",
pages = "273--286",
abstract = "With the fast pace of reporting around the globe from various sources, event extraction has increasingly become an important task in NLP. The use of pre-trained language models (PTMs) has become popular to provide contextual representation for downstream tasks. This work aims to pre-train language models that enhance event extraction accuracy. To this end, we propose an Event-Based Knowledge (EBK) masking approach to mask the most significant terms in the event detection task. These significant terms are based on an external knowledge source that is curated for the purpose of event detection for the Arabic language. The proposed approach improves the classification accuracy of all the 9 event types. The experimental results demonstrate the effectiveness of the proposed masking approach and encourage further exploration.",
}
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<abstract>With the fast pace of reporting around the globe from various sources, event extraction has increasingly become an important task in NLP. The use of pre-trained language models (PTMs) has become popular to provide contextual representation for downstream tasks. This work aims to pre-train language models that enhance event extraction accuracy. To this end, we propose an Event-Based Knowledge (EBK) masking approach to mask the most significant terms in the event detection task. These significant terms are based on an external knowledge source that is curated for the purpose of event detection for the Arabic language. The proposed approach improves the classification accuracy of all the 9 event types. The experimental results demonstrate the effectiveness of the proposed masking approach and encourage further exploration.</abstract>
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%0 Conference Proceedings
%T Event-Based Knowledge MLM for Arabic Event Detection
%A Yamani, Asma Z.
%A Alsulami, Amjad K.
%A Al-Zaidy, Rabeah A.
%Y Bouamor, Houda
%Y Al-Khalifa, Hend
%Y Darwish, Kareem
%Y Rambow, Owen
%Y Bougares, Fethi
%Y Abdelali, Ahmed
%Y Tomeh, Nadi
%Y Khalifa, Salam
%Y Zaghouani, Wajdi
%S Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F yamani-etal-2022-event
%X With the fast pace of reporting around the globe from various sources, event extraction has increasingly become an important task in NLP. The use of pre-trained language models (PTMs) has become popular to provide contextual representation for downstream tasks. This work aims to pre-train language models that enhance event extraction accuracy. To this end, we propose an Event-Based Knowledge (EBK) masking approach to mask the most significant terms in the event detection task. These significant terms are based on an external knowledge source that is curated for the purpose of event detection for the Arabic language. The proposed approach improves the classification accuracy of all the 9 event types. The experimental results demonstrate the effectiveness of the proposed masking approach and encourage further exploration.
%R 10.18653/v1/2022.wanlp-1.25
%U https://aclanthology.org/2022.wanlp-1.25
%U https://doi.org/10.18653/v1/2022.wanlp-1.25
%P 273-286
Markdown (Informal)
[Event-Based Knowledge MLM for Arabic Event Detection](https://aclanthology.org/2022.wanlp-1.25) (Yamani et al., WANLP 2022)
ACL
- Asma Z Yamani, Amjad K Alsulami, and Rabeah A Al-Zaidy. 2022. Event-Based Knowledge MLM for Arabic Event Detection. In Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP), pages 273–286, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.