PROTEST-ER: Retraining BERT for Protest Event Extraction

Tommaso Caselli, Osman Mutlu, Angelo Basile, Ali Hürriyetoğlu


Abstract
We analyze the effect of further retraining BERT with different domain specific data as an unsupervised domain adaptation strategy for event extraction. Portability of event extraction models is particularly challenging, with large performance drops affecting data on the same text genres (e.g., news). We present PROTEST-ER, a retrained BERT model for protest event extraction. PROTEST-ER outperforms a corresponding generic BERT on out-of-domain data of 8.1 points. Our best performing models reach 51.91-46.39 F1 across both domains.
Anthology ID:
2021.case-1.4
Volume:
Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | CASE | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12–19
Language:
URL:
https://aclanthology.org/2021.case-1.4
DOI:
10.18653/v1/2021.case-1.4
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.case-1.4.pdf