yasaminal at Semeval2026: Constraint-Aware Humor Generation with Knowledge Graph Guidance

AbstractThis paper presents a knowledge-guided humor generation system, which involves generating humorous text from either a pair of words or a news headline. The proposed approach integrates structured semantic reasoning derived from a knowledge graph (KG) with controlled generation using large language models (LLMs). The system constructs an intermediate KG hint consisting of related concepts retrieved in the target language, which is appended to the prompt to guide the generation process in a structured manner. A single candidate joke is generated per input using controlled top-p decoding. Experimental results show that incorporating KG reasoning improves relevance and constraint satisfaction, while LLM-based generation ensures fluency and creativity. Overall, the proposed method offers a structured and interpretable framework for humor generation across multiple languages.