GPF: Generative Prediction Fusion for Multi-Label Emotion Classification
Shiqiao Huang, Weiwen Zhang, Nankai Lin, Mianshen Xu · 2024
Multi-label Emotion Classification (MEC) is a fundamental and challenging task in natural language processing. The MEC task aims to recognize at least an emotion from a sentence. Previous seq2seq based models required to transform the set of labels into a sequence. However, the labels in the sentence are unordered. In this paper, we propose GPF as a framework of generative prediction fusion for MEC. Specifically, we utilize the non-autoregressive decoder to simultaneously generate the set of labels and propose an output fusion strategy for MEC. Meanwhile, we develop a multi-label contrastive learning to enhance the representation of our model. Experiment results on three distinct language datasets demonstrate the effectiveness of our model.