ExPUNations: Augmenting Puns with Keywords and Explanations
This work addresses the problem of enhancing AI's ability to handle humor, specifically puns, for natural language processing applications, though it is incremental as it builds on existing datasets and tasks.
The authors tackled the challenge of humor understanding and generation by creating the ExPUNations dataset, which augments puns with crowdsourced keywords, explanations, and funniness ratings, and showed that these annotations improve humor classifier accuracy and robustness and aid in generating better humorous texts in human evaluations.
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.