Publication: PROTEST-ER: retraining BERT for protest event extraction
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Caselli, Tommaso
Basile, Angelo
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We analyze the effect of further pre-training 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.
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Association for Computational Linguistics (ACL)
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Computer science, Artificial intelligence, Linguistics
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Case 2021: The 4th Workshop On Challenges And Applications Of Automated Extraction Of Socio-Political Events From Text (Case)
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