对BERT进行抗议事件提取的再训练

Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021) Pub Date : 1900-01-01 DOI:10.18653/v1/2021.case-1.4

Tommaso Caselli, Osman Mutlu, A. Basile, Ali Hürriyetoǧlu

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引用次数: 10

摘要

我们分析了使用不同领域特定数据进一步再训练BERT作为事件提取的无监督领域自适应策略的效果。事件提取模型的可移植性尤其具有挑战性，在相同文本类型(例如，新闻)上影响数据的性能下降很大。我们提出了一种用于抗议事件提取的再训练BERT模型——protest - er。PROTEST-ER在域外数据上的表现优于相应的通用BERT，得分为8.1分。我们最好的模型在这两个领域达到51.91-46.39 F1。

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PROTEST-ER: Retraining BERT for Protest Event Extraction

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.

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Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)

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