Leveraging Label Correlations in a Multi-label Setting: A Case Study in Emotion

• URL: http://arxiv.org/abs/2210.15842v1
• Date: Fri, 28 Oct 2022 02:27:18 GMT
• Title: Leveraging Label Correlations in a Multi-label Setting: A Case Study in Emotion
• Authors: Georgios Chochlakis (1 and 2), Gireesh Mahajan (3), Sabyasachee Baruah (1 and 2), Keith Burghardt (2), Kristina Lerman (2), Shrikanth Narayanan (1 and 2) ((1) Signal Analysis and Interpretation Lab, University of Southern California, (2) Information Science Institute, University of Southern California, (3) Microsoft Cognitive Services)
• Abstract summary: We exploit label correlations in multi-label emotion recognition models to improve emotion detection. We demonstrate state-of-the-art performance across Spanish, English, and Arabic in SemEval 2018 Task 1 E-c using monolingual BERT-based models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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