E x PUN ations: Augmenting Puns with Keywords and Explanations (2022)

Abstract: The tasks of humor understanding and generation are challenging and subjective even for humans, requiring commonsense and real-world knowledge to master. Puns, in particular, add the challenge of fusing that knowledge with the ability to interpret lexical-semantic ambiguity. In this paper, we present the ExPUNations (ExPUN) dataset, in which we augment an existing dataset of puns with detailed crowdsourced annotations of keywords denoting the most distinctive words that make the text funny, pun explanations describing why the text is funny, and fine-grained funniness ratings. This is the first humor dataset with such extensive and fine-grained annotations specifically 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 understanding and generation models’ ability to understand and generate humor. We showcase that the annotated keywords we collect are helpful for generating better novel humorous texts in human evaluation, and that our natural language explanations can be leveraged to improve both the accuracy and robustness of humor classifiers.

## introduction humor serves multiple purposes and provides numerous benefits, such as relieving anxiety, avoiding painful feelings and facilitating learning @xcite . as a specific example of humor, the creative uses of puns, wordplay and ambiguity are important ways to come up with jokes @xcite . 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 ## expun dataset in this section, we describe our data annotation procedure, including details of the annotation fields and our assessment of the annotation quality. 1 resources will be available at: https://github. com/amazon-research/expunations ## experiments with the collected annotations, we propose two new tasks, pun explanation and keyword conditioned pun generation, to showcase novel tasks that our dataset uniquely enables and push the frontiers of nlu and nlg for humor. note that the rich annotations in expun can also enable many other interesting tasks such us pun keywords extraction, fine-grained funniness prediction, and others. however, we prioritize nlg tasks as they are relatively under-explored compared to nlu tasks. in this section, we benchmark current state-of-the-art models' performance on the proposed tasks. ## related work in this work, we contribute annotations for a humor dataset as well as two humor-related generation tasks. the work is broadly related to pun generation, pun detection, explanation generation, and humor generation. we briefly summarize works in these directions. ## conclusion in this paper, we contribute a dataset of extensive, high-quality annotations of humor explanation, keywords, and fine-grained funniness ratings. this is the first humor dataset with such extensive and finegrained annotations. based on the annotations, we propose two tasks: pun explanation and keywordconditioned pun generation, to challenge state-ofthe-art natural language understanding and generation models' ability to understand and generate humorous text. we benchmark several strong models' performances on the two proposed tasks to validate the practical usage of the proposed annotations, and show that our human-annotated explanations and keywords are beneficial in understanding and generating humor. future directions include a deeper analysis of how to characterize pun explanation more objectively within our annotation scheme, as well as further exploration of better models for both the pun explanation and pun generation tasks.