Humor detection has emerged as an active research area within
the field of artificial intelligence. Over the past few decades, it has
made remarkable progress with the development of deep learning.
This paper introduces a novel framework aimed at enhancing the
model’s understanding of humorous expressions. Specifically, we
consider the impact of correspondence between labels and features.
In order to achieve more effective models with limited training
samples, we employ a widely utilized semi-supervised learning
technique called pseudo labeling. Furthermore, we use the postsmoothing strategy to eliminate abnormally high predictions. At
the same time, in order to alleviate the over-fitting phenomenon
of the model on the validation set, we created 10 different random
subsets of the training and then aggregating their prediction. To
verify the effectiveness of our strategy, we evaluate its performance
on the Cross-Cultural Humour sub-challenge at MuSe 2023. Experimental results demonstrate that our system achieves an AUC
score of 0.9112, surpassing the performance of baseline models by
a substantial margin.