PunGraph：检索增强的音义图推理用于双关语理解

中文摘要 AI 辅助

双关语是一种具有挑战性的比喻性语言形式，它利用语音相似性和语义歧义来传达多重含义。尽管大型语言模型（LLMs）展现出强大的语言理解能力，但由于有限的语音建模和不受控制的端到端生成，它们在双关语推理方面仍然存在困难。我们提出了PunGraph，一个用于双关语理解的检索增强知识图谱框架。PunGraph利用Unisyn语音词典、IPA和G2P表示以及WordNet定义构建了一个音义词汇图，并检索候选词或词义以将LLM推理限制在结构化的候选空间内。我们进一步引入了WebPun，一个包含5,730个带注释的异形双关语和同形双关语的新大规模数据集。在SemEval-2017和WebPun上的实验表明，PunGraph持续提升了小规模LLM的性能，并取得了与强大专有模型相竞争的结果。进一步的分析表明，检索引导的语音和语义约束有效减少了双关语解释中的常见推理错误，凸显了将结构化知识与LLM集成的益处。我们在该https URL发布了代码和数据集。

英文摘要

Puns are a challenging form of figurative language that exploit phonetic similarity and semantic ambiguity to convey multiple meanings. Although large language models (LLMs) demonstrate strong language understanding capabilities, they still struggle with pun reasoning due to limited phonetic modeling and uncontrolled end-to-end generation. We propose \textbf{PunGraph}, a retrieval-enhanced knowledge graph framework for pun understanding. PunGraph constructs a phonetic-semantic lexical graph using the Unisyn phonetic dictionary, IPA and G2P representations, and WordNet definitions, and retrieves candidate words or senses to constrain LLM reasoning within a structured candidate space. We further introduce \textbf{WebPun}, a new large-scale dataset containing 5,730 annotated heterographic and homographic puns. Experiments on SemEval-2017 and WebPun show that PunGraph consistently improves the performance of small-scale LLMs and achieves competitive results against strong proprietary models. Further analysis shows that retrieval-guided phonetic and semantic constraints effectively reduce common reasoning errors in pun interpretation, highlighting the benefits of integrating structured knowledge with LLMs. We release our code and dataset at https://github.com/ysu132/PunGraph.

发表机构

• University of Auckland（奥克兰大学）
• Shanxi University（山西大学）
• Queen Mary University of London（伦敦玛丽女王大学）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

EMNLP2026 Main Conference