论文标题
在多标签环境中利用标签相关性：情感案例研究
Leveraging Label Correlations in a Multi-label Setting: A Case Study in Emotion
论文作者
论文摘要
在文本中表达的情绪对一系列领域至关重要。在这项工作中，我们研究了在多标签情感识别模型中利用标签相关性的方法，以改善情绪检测。首先，我们为问题开发了两种建模方法，以通过包括输入中的情感或利用蒙版语言建模（MLM）来捕获情感单词本身的单词关联。其次，我们将情绪表示形式的成对约束作为正则术语与模型的分类损失一起。我们将这些条款分为本地和全球两类。前者根据黄金标签动态变化，而后者在训练过程中保持静态。我们使用基于BERT的模型在Semeval 2018 Task 1 E-C中展示了西班牙语，英语和阿拉伯语的最先进的表现。除了更好的性能之外，我们还表现出改善的鲁棒性。代码可在https://github.com/gchochla/demux-memo上找到。
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.
