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. 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 https://github.com/Gzhang-umich/1CademyTeamOfCASE.

本文介绍了我们参加EMNLP 2022的CASE研讨会的情况，利用自我训练方法解决了事件因果关系检测的问题，并验证了在不同训练集数量下模型性能的变化，结果表明自我训练可以显著提高模型性能，代码可在此https链接中公开获取。

1Cademy @ Causal News Corpus 2022: 在社会政治事件数据的因果分类中利用自训练