Most few-shot learning approaches can’t get satisfactory results in fine-grained multi-category sentiment classification tasks. To solve this problem, a joint learning approach is proposed to few-shot learning for multi-category sentiment classification. Specifically, we utilize the pre-trained token-replaced detection model as few-shot learners and concurrently reformulate fine-grained sentiment classification tasks as both classification and regression problems by appending classification and regression templates and label description words to the input at the same time. For joint learning, several fusion methods are proposed to fuse the classification prediction and regression prediction. Experimental results show that, compared to mainstream few-shot methods, the proposed approach apparently achieves better performances in F1-Score and accuracy rate.

对于多类别的细粒度情感分类任务, 目前主流的少样本学习方法不能取得较好的性能。针对这一问题, 提出一种基于联合学习的少样本多类别情感分类方法。采用基于替换词检测任务的少样本学习方式, 将回归和分类的替换词检测模板以及标签描述词同时添加至输入语句, 从而将细粒度情感分类任务同时建模为分类问题和回归问题。在此基础上, 设计了不同的融合方法进行联合学习。实验结果表明, 与主流少样本学习方法相比, 该方法在 F1-Score 和正确率上都取得更优的结果。