1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality Classification of Socio-Political Event Data

Adam Nik, Ge Zhang, Xingran Chen, Mingyu Li, Jie Fu

Abstract
This paper details our participation in the Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) workshop @ EMNLP 2022, where we take part in Subtask 1 of Shared Task 3 {citep{tan-etal-2022-event}. We approach the given task of event causality detection by proposing a self-training pipeline that follows a teacher-student classifier method. More specifically, we initially train a teacher model on the true, original task data, and use that teacher model to self-label data to be used in the training of a separate student model for the final task prediction. We test how restricting the number of positive or negative self-labeled examples in the self-training process affects classification performance. Our final results show that using self-training produces a comprehensive performance improvement across all models and self-labeled training sets tested within the task of event causality sequence classification. On top of that, we find that self-training performance did not diminish even when restricting either positive/negative examples used in training.Our code is be publicly available at {hyperlink{https://github.com/Gzhang-umich/1CademyTeamOfCASE}{https://github.com/Gzhang-umich/1CademyTeamOfCASE}.
Anthology ID:
2022.case-1.13
Volume:
Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Hybrid)
Venue:
CASE
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
91–99
Language:
URL:
https://aclanthology.org/2022.case-1.13
DOI:
Bibkey:
Cite (ACL):
Adam Nik, Ge Zhang, Xingran Chen, Mingyu Li, and Jie Fu. 2022. 1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality Classification of Socio-Political Event Data. In Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE), pages 91–99, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality Classification of Socio-Political Event Data (Nik et al., CASE 2022)
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