ExPUNations: Augmenting Puns with Keywords and Explanations

TL;DR

This paper introduces the ExPUNations dataset with detailed annotations for puns, enabling improved humor understanding and generation through explanation and keyword-based tasks, advancing NLP models' humor capabilities.

Contribution

It presents the first extensive, fine-grained humor dataset for puns, with annotations that facilitate explanation generation and keyword-conditioned pun creation, enhancing humor NLP tasks.

Findings

Annotated keywords improve humorous text generation.

Explanations enhance humor classifier accuracy and robustness.

Dataset enables new humor understanding and generation tasks.

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.