Fine-Grained Emotion Prediction by Modeling Emotion Definitions
| Autor: | Piyush Rai, Ashutosh Modi, Gargi Singh, Dhanajit Brahma |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Computer Science - Artificial Intelligence business.industry Generalization Computer science Machine learning computer.software_genre Class (biology) Task (project management) Artificial Intelligence (cs.AI) Taxonomy (general) Benchmark (computing) Artificial intelligence Language model Affective computing business Transfer of learning Computation and Language (cs.CL) computer |
| Zdroj: | ACII |
| Popis: | In this paper, we propose a new framework for fine-grained emotion prediction in the text through emotion definition modeling. Our approach involves a multi-task learning framework that models definitions of emotions as an auxiliary task while being trained on the primary task of emotion prediction. We model definitions using masked language modeling and class definition prediction tasks. Our models outperform existing state-of-the-art for fine-grained emotion dataset GoEmotions. We further show that this trained model can be used for transfer learning on other benchmark datasets in emotion prediction with varying emotion label sets, domains, and sizes. The proposed models outperform the baselines on transfer learning experiments demonstrating the generalization capability of the models. Comment: 8 Pages, accepted at ACII 2021 for Orals |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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