@inproceedings{su-etal-2026-pungraph,
title = "{P}un{G}raph: Retrieval-Enhanced Phonetic-Semantic Graph Reasoning for Pun Understanding",
author = "Su, Yuchen and
Huang, Zijian and
Shi, Yaotian and
Zhong, Shaoxin and
Wang, Ruofan and
Li, Mengze and
Zhu, Yonghua and
Benavides-Prado, Diana and
Witbrock, Michael J.",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.822/",
pages = "19111--19124",
ISBN = "979-8-89176-459-0",
abstract = "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 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 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."
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<abstract>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 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 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.</abstract>
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%0 Conference Proceedings
%T PunGraph: Retrieval-Enhanced Phonetic-Semantic Graph Reasoning for Pun Understanding
%A Su, Yuchen
%A Huang, Zijian
%A Shi, Yaotian
%A Zhong, Shaoxin
%A Wang, Ruofan
%A Li, Mengze
%A Zhu, Yonghua
%A Benavides-Prado, Diana
%A Witbrock, Michael J.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F su-etal-2026-pungraph
%X 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 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 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.
%U https://aclanthology.org/2026.emnlp-main.822/
%P 19111-19124
Markdown (Informal)
[PunGraph: Retrieval-Enhanced Phonetic-Semantic Graph Reasoning for Pun Understanding](https://aclanthology.org/2026.emnlp-main.822/) (Su et al., EMNLP 2026)
ACL
- Yuchen Su, Zijian Huang, Yaotian Shi, Shaoxin Zhong, Ruofan Wang, Mengze Li, Yonghua Zhu, Diana Benavides-Prado, and Michael J. Witbrock. 2026. PunGraph: Retrieval-Enhanced Phonetic-Semantic Graph Reasoning for Pun Understanding. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 19111–19124, Budapest, Hungary. Association for Computational Linguistics.