论文标题
部分可观测时空混沌系统的无模型预测
Leveraging Emotion-specific Features to Improve Transformer Performance for Emotion Classification
论文作者
论文摘要
本文描述了PVGS AI俱乐部在WASSA 2022上执行的情感分类的方法。该曲目2子任务的重点是建立模型，这些模型可以根据新闻文章的论文来预测多级情感标签，其中一个人，团体或其他实体受到影响。基线变压器模型一直在序列分类任务上展示良好的结果，我们旨在通过结合技术的帮助，并利用两种特定于情绪的表示。我们观察到的结果比基线模型更好，并且在情绪分类任务上的精度为0.619，宏F1得分为0.520。
This paper describes the approach to the Emotion Classification shared task held at WASSA 2022 by team PVGs AI Club. This Track 2 sub-task focuses on building models which can predict a multi-class emotion label based on essays from news articles where a person, group or another entity is affected. Baseline transformer models have been demonstrating good results on sequence classification tasks, and we aim to improve this performance with the help of ensembling techniques, and by leveraging two variations of emotion-specific representations. We observe better results than our baseline models and achieve an accuracy of 0.619 and a macro F1 score of 0.520 on the emotion classification task.
