New Benchmark Corpus and Models for Fine-grained Event Classification: To BERT or not to BERT?

Jakub Piskorski, Jacek Haneczok, Guillaume Jacquet


Abstract
We introduce a new set of benchmark datasets derived from ACLED data for fine-grained event classification and compare the performance of various state-of-the-art models on these datasets, including SVM based on TF-IDF character n-grams and neural context-free embeddings (GLOVE and FASTTEXT) as well as deep learning-based BERT with its contextual embeddings. The best results in terms of micro (94.3-94.9%) and macro F1 (86.0-88.9%) were obtained using BERT transformer, with simpler TF-IDF character n-gram based SVM being an interesting alternative. Further, we discuss the pros and cons of the considered benchmark models in terms of their robustness and the dependence of the classification performance on the size of training data.
Anthology ID:
2020.coling-main.584
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6663–6678
Language:
URL:
https://aclanthology.org/2020.coling-main.584
DOI:
10.18653/v1/2020.coling-main.584
Bibkey:
Cite (ACL):
Jakub Piskorski, Jacek Haneczok, and Guillaume Jacquet. 2020. New Benchmark Corpus and Models for Fine-grained Event Classification: To BERT or not to BERT?. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6663–6678, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
New Benchmark Corpus and Models for Fine-grained Event Classification: To BERT or not to BERT? (Piskorski et al., COLING 2020)
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PDF:
https://aclanthology.org/2020.coling-main.584.pdf