  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.
