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    <titleInfo>
        <title>Homophonic Pun Generation in Code Mixed Hindi English</title>
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        <namePart type="given">Yash</namePart>
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            <title>Proceedings of the 1st Workshop on Computational Humor (CHum)</title>
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        <name type="personal">
            <namePart type="given">Christian</namePart>
            <namePart type="given">F</namePart>
            <namePart type="family">Hempelmann</namePart>
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        <name type="personal">
            <namePart type="given">Tiansi</namePart>
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    <abstract>In this study, we investigate Hinglish—a blend of Hindi and English commonly found in informal online communication—with a particular focus on automated pun generation. Our work examines the applicability and adaptability of existing English pun generation pipelines to Hinglish. We assess the pun generation capabilities of Large Language Models (LLMs), particularly GPT-3.5. By employing Chain of Thought prompting and Self-Refine techniques, we identify cross-linguistic homophone detection as a central difficulty. To address this, we propose a novel algorithm for cross-lingual homophone identification and develop a Latin-to-Devanagari transliteration module to leverage the widespread use of Latin-script Hindi in online settings. Building on existing frameworks for pun generation, we incorporate our homophone and transliteration modules to improve output quality. Crowd-sourced human evaluations validate the effectiveness of our approach.</abstract>
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        <url>https://aclanthology.org/2025.chum-1.4/</url>
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        <date>2025-01</date>
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%0 Conference Proceedings
%T Homophonic Pun Generation in Code Mixed Hindi English
%A Sarrof, Yash Raj
%Y Hempelmann, Christian F.
%Y Rayz, Julia
%Y Dong, Tiansi
%Y Miller, Tristan
%S Proceedings of the 1st Workshop on Computational Humor (CHum)
%D 2025
%8 January
%I Association for Computational Linguistics
%C Online
%F sarrof-2025-homophonic
%X In this study, we investigate Hinglish—a blend of Hindi and English commonly found in informal online communication—with a particular focus on automated pun generation. Our work examines the applicability and adaptability of existing English pun generation pipelines to Hinglish. We assess the pun generation capabilities of Large Language Models (LLMs), particularly GPT-3.5. By employing Chain of Thought prompting and Self-Refine techniques, we identify cross-linguistic homophone detection as a central difficulty. To address this, we propose a novel algorithm for cross-lingual homophone identification and develop a Latin-to-Devanagari transliteration module to leverage the widespread use of Latin-script Hindi in online settings. Building on existing frameworks for pun generation, we incorporate our homophone and transliteration modules to improve output quality. Crowd-sourced human evaluations validate the effectiveness of our approach.
%U https://aclanthology.org/2025.chum-1.4/
%P 23-31

Markdown (Informal)

[Homophonic Pun Generation in Code Mixed Hindi English](https://aclanthology.org/2025.chum-1.4/) (Sarrof, chum 2025)

• Homophonic Pun Generation in Code Mixed Hindi English (Sarrof, chum 2025)

ACL

• Yash Raj Sarrof. 2025. Homophonic Pun Generation in Code Mixed Hindi English. In Proceedings of the 1st Workshop on Computational Humor (CHum), pages 23–31, Online. Association for Computational Linguistics.