Humor Level Recognition Based on Prompt Learning and Contrastive Learning.

Humor recognition is one of the hot topics in the field of natural language processing in recent years. Most existing methods focus on whether something is humorous, while there is less research on humor level recognition. This article proposes a method based on prompt learning and contrastive learning for humor level recognition. Firstly, the context with prompt learning templates and the context without prompt learning templates are input into a pre-trained language model separately. Then, during the fine-tuning stage, a prompt learning strategy based on P-tuning is used to learn semantic information related to humor levels in the text. Subsequently, a contrastive learning loss function is introduced to increase the distance between the vector representations of different classes and reduce the classification difficulty of strong humor and weak humor samples. Finally, a multi-task learning strategy is used to simultaneously perform prompt learning, contrastive learning, and sentence binary classification tasks. Experiments on the Reddit public humor dataset show that the model’s accuracy on the corpus improves by 1.1% compared to the previous best results. Experimental results indicate that the model proposed in this article can effectively recognize humor levels.