Context-Situated Pun Generation

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4635 - 4648 December 7-11, 2022 ©2022 Association for Computational Linguistics Context-Situated Pun Generation Jiao Sun1∗ Anjali Narayan-Chen2 Shereen Oraby2 Shuyang Gao2† Tagyoung Chung2 Jing Huang2 Yang Liu2 Nanyun Peng2,3 1University of Southern California 2Amazon Alexa AI 3University of California, Los Angeles jiaosun@usc.edu {naraanja,orabys,shuyag,tagyoung,jhuangz,yangliud}@amazon.com violetpeng@cs.ucla.edu Abstract Previous work on pun generation commonly begins with a given pun word (a pair of homo- phones for heterographic pun generation and a polyseme for homographic pun generation) and seeks to generate an appropriate pun. While this may enable efficient pun generation, we believe that a pun is most entertaining if it fits appropriately within a given context, e.g., a given situation or dialogue. In this work, we propose a new task, context-situated pun gen- eration, where a specific context represented by a set of keywords is provided, and the task is to first identify suitable pun words that are appropriate for the context, then generate puns based on the context keywords and the identi- fied pun words. We collect � CUP (Context- sitUated Pun), containing 4.5k tuples of con- text words and pun pairs. Based on the new data and setup, we propose a pipeline system for context-situated pun generation, including a pun word retrieval module that identifies suit- able pun words for a given context, and a gener- ation module that generates puns from context keywords and pun words. Human evaluation shows that 69% of our top retrieved pun words can be used to generate context-situated puns, and our generation module yields successful puns 31% of the time given a plausible tuple of context words and pun pair, almost tripling the yield of a state-of-the-art pun generation model. With an end-to-end evaluation, our pipeline sys- tem with the top-1 retrieved pun pair for a given context can generate successful puns 40% of the time, better than all other modeling varia- tions but 32% lower than the human success rate. This highlights the difficulty of the task, and encourages more research in this direction. 1 Introduction Pun generation is a challenging creative genera- tion task that has attracted some recent attention in the research community (He et al., 2019; Yu et al., ∗Work done during Jiao’s internship at Amazon. †Work done while Shuyang was at Amazon. Figure 1: Context-situated pun generation aims to find relevant pun words to generate puns within a given context. We propose a unified framework to generate both homographic and heterographic puns; examples shown here are human-written puns from our corpus. 2018, 2020; Mittal et al., 2022; Horri, 2011). As one of the most important ways to communicate humor (Abbas and Dhiaa, 2016), puns can help relieve anxiety, avoid painful feelings and facilitate learning (Buxman, 2008). At the same time, spon- taneity is the twin concept of creativity (Moreno, 1955), which means the context matters greatly for making an appropriate and funny pun. Existing work on pun generation mainly focuses on generating puns given a pair …

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