Detecting emotions expressed in text has become critical to a range of fields. In this work, we investigate ways to exploit label correlations in multi-label emotion recognition models to improve emotion detection. First, we develop two modeling approaches to the problem in order to capture word associations of the emotion words themselves, by either including the emotions in the input, or by leveraging Masked Language Modeling (MLM). Second, we integrate pairwise constraints of emotion representations as regularization terms alongside the classification loss of the models. We split these terms into two categories, local and global. The former dynamically change based on the gold labels, while the latter remain static during training. We demonstrate state-of-the-art performance across Spanish, English, and Arabic in SemEval 2018 Task 1 E-c using monolingual BERT-based models. On top of better performance, we also demonstrate improved robustness. Code is available at https://github.com/gchochla/Demux-MEmo.

翻译：检测文本中表达的情绪已成为多个领域的关键任务。本研究探索在多标签情绪识别模型中利用标签相关性的方法以提升情绪检测效果。首先，我们开发了两种建模方法来捕捉情绪词本身的词汇关联：一种是将情绪标签纳入输入，另一种是利用掩码语言建模（MLM）。其次，我们将情绪表征的成对约束作为正则化项，与模型分类损失函数相结合。我们将这些约束项分为局部和全局两类：前者根据真实标签动态调整，后者在训练过程中保持静态。我们基于单语BERT模型在SemEval 2018任务1 E-c数据集上展示了西班牙语、英语和阿拉伯语的最新表现。除了性能提升，我们还验证了模型鲁棒性的增强。代码已开源至https://github.com/gchochla/Demux-MEmo。