arXiv AI

Pun Intended: Multi-Agent Translation of Wordplay with Contrastive Learning and Phonetic-Semantic Embeddings for CLEF JOKER 2025 Task 2

arXiv:2507. 06506v2 Announce Type: replace-cross Abstract: Translating wordplay across languages presents unique challenges that have long confounded both professional human translators and machine translation systems.

arXiv AI
Aug 28

TransMeme: A Multi-Agent Framework for Cross-Cultural Meme Transcreation

TransMeme introduces a multi‑agent framework for cross‑cultural meme transcreation, addressing the unique challenges of preserving intent, adapting cultural meaning, and maintaining multimodal consistency. The system coordinates specialized agents for cultural adaptation, text rewriting, revision, and visual adjustment, and is evaluated on Chinese‑English meme pairs. Human and LLM‑based evaluations show that TransMeme outperforms baselines, achieving a 33.1% average improvement in human scores and a 60% Top‑1 ranking rate in LLM judgments.

By Jingyi Zheng, Yule Liu, Zifan Peng, Tianyi Hu, Yuemeng Zhao, Xinhu Zheng, Xinlei He
arXiv Computation and Language
Sep 16

PunGraph: Retrieval-Enhanced Phonetic-Semantic Graph Reasoning for Pun Understanding

PunGraph is a retrieval‑enhanced knowledge‑graph framework designed to improve pun understanding by combining phonetic and semantic information. It builds a phonetic‑semantic lexical graph using the Unisyn phonetic dictionary, IPA and G2P representations, and WordNet definitions, and uses this graph to retrieve candidate words or senses that constrain large language model reasoning. The authors also introduce WebPun, a new dataset of 5,730 annotated heterographic and homographic puns, and demonstrate that PunGraph consistently boosts the performance of small‑scale LLMs on SemEval‑2017 and WebPun, achieving results competitive with strong proprietary models.

By Yuchen Su, Zijian Huang, Yaotian Shi, Shaoxin Zhong, Ruofan Wang, Mengze Li, Yonghua Zhu, Diana Benavides-Prado, Michael Witbrock
arXiv Computation and Language
Aug 24

Jokes Aside: Measuring the Semantic Distance of Double Meanings

The paper investigates how semantic distance and ambiguity contribute to joke humor by revisiting and extending metrics from prior work. It introduces a new symmetry metric—measuring how close the ambiguous element Z is to both X and Y—and evaluates it using two embedding models on three joke datasets, including expanded versions with paired ambiguous sentences. Although models based on these metrics performed poorly in predicting humor ratings, the symmetry metric consistently correlated with higher-rated jokes, hinting it captures a key, though not sole, property of humor.

By Fabio De Ponte