ExPUNations: Augmenting Puns with Keywords and Explanations

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4590 - 4605 December 7-11, 2022 ©2022 Association for Computational Linguistics ExPUNations: Augmenting Puns with Keywords and Explanations Jiao Sun1∗† Anjali Narayan-Chen2† Shereen Oraby2 Alessandra Cervone2 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,cervon,tagyoung,jhuangz,yangliud}@amazon.com violetpeng@cs.ucla.edu Abstract The tasks of humor understanding and gen- eration are challenging and subjective even for humans, requiring commonsense and real- world knowledge to master. Puns, in particu- lar, add the challenge of fusing that knowledge with the ability to interpret lexical-semantic ambiguity. In this paper, we present the Ex- PUNations (ExPUN) dataset, in which we aug- ment an existing dataset of puns with detailed crowdsourced annotations of keywords denot- ing the most distinctive words that make the text funny, pun explanations describing why the text is funny, and fine-grained funniness rat- ings. This is the first humor dataset with such extensive and fine-grained annotations specif- ically for puns. Based on these annotations, we propose two tasks: explanation generation to aid with pun classification and keyword- conditioned pun generation, to challenge the current state-of-the-art natural language under- standing and generation models’ ability to un- derstand and generate humor. We showcase that the annotated keywords we collect are help- ful for generating better novel humorous texts in human evaluation, and that our natural lan- guage explanations can be leveraged to improve both the accuracy and robustness of humor clas- sifiers. 1 Introduction Humor serves multiple purposes and provides nu- merous benefits, such as relieving anxiety, avoiding painful feelings and facilitating learning (Buxman, 2008). As a specific example of humor, the creative uses of puns, wordplay and ambiguity are impor- tant ways to come up with jokes (Chiaro, 2006). Pun understanding and generation are particularly challenging tasks because they require extensive commonsense and world knowledge to compose and understand, even for humans. Despite growing ∗Work done during Jiao’s internship at Amazon. †Both authors equally contributed to the paper. Text When artists dream in color it’s a pigment of their imagination. KWD artists , dream , color , pigment , imagination . NLEx Pigments are non-soluble materials often used in painting, and pigment sounds like figment, which is something that is not real but someone believes it is. Text The man found something to catch fish, which was a net gain. KWD catch fish , net gain . NLEx This is a play on words. A “net gain” means an increase in revenue but here “net” refers to how a net is used to catch fish. Table 1: Two examples of annotated Keywords (KWD) and Natural Language Explanations (NLEx) for puns in our dataset. The highlighted texts are annotated keywords that contribute to making the text funny. interest in the area, there are limited amounts of data available in the domain of humor understand- ing and generation. Existing humor datasets are usually only anno- tated with binary labels indicating whether each sentence …

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