  Humor, a culturally nuanced aspect of human language, poses challenges for
computational understanding and generation, especially in Chinese humor, which
remains relatively unexplored in the NLP community. This paper investigates the
capability of state-of-the-art language models to comprehend and generate
Chinese humor, specifically focusing on training them to create allegorical
sayings. We employ two prominent training methods: fine-tuning a medium-sized
language model and prompting a large one. Our novel fine-tuning approach
incorporates fused Pinyin embeddings to consider homophones and employs
contrastive learning with synthetic hard negatives to distinguish humor
elements. Human-annotated results show that these models can generate humorous
allegorical sayings, with prompting proving to be a practical and effective
method. However, there is still room for improvement in generating allegorical
sayings that match human creativity.
