Multi-label emotion classification based on adversarial multi-task learning

Abstract

In this paper, we focus on the task of multi-label emotion classification and aim to tackle two problems of this task. First, few studies try to exploit the correlation among different emotions, which motivates us to introduce the task-specific information into the shared hidden layer. Second, the public multi-label emotion datasets for low-resource languages are limited. To overcome these problems, we propose a novel multi-task multi-label emotion classification. Our approach consists of three components: general representation module, emotion representation module and adversarial classifier. The model applies emotion descriptors to incorporate the correlation among different emotions, and then uses adversarial training to prevent too much emotion-relevant information from being injected into the shared layer. Extensive experiments demonstrate that our approach outperforms the state-of-the-art baselines across a variety of evaluation metrics, achieving macro-average F1 scores of 50.21%, 41.33% and 40.24% on the Chinese, English, and Indonesian datasets, respectively. In addition, an Indonesian dataset and an English one containing 4207 and 26,019 samples respectively, are constructed for the multi-label emotion classification task. The datasets will be publicly available and we believe this can support the future study of Indonesian multi-label emotion recognition resources, which are limited for the related research fields now. We make our codes and resources in this work publicly available on: https://github.com/GKLMIP/MLEC-AML.