Despite recent advances in information retrieval and natural language processing, rhetorical devices that exploit ambiguity or subvert linguistic rules remain a challenge for such systems. However, corpus-based analysis of wordplay has been a perennial topic of scholarship in the humanities, including literary criticism, language education, and translation studies. The immense data-gathering effort required for these studies points to the need for specialized text retrieval and classification technology, and consequently for appropriate test collections. In this paper, we introduce and analyze a new dataset for research and applications in the retrieval and processing of wordplay. Developed for the JOKER track at CLEF 2023, our annotated corpus extends and improves upon past English wordplay detection datasets in several ways. First, we introduce hundreds of additional positive examples of wordplay; second, we provide French translations for the examples; and third, we provide negative examples of non-wordplay with characteristics closely matching those of the positive examples. This last feature helps ensure that AI models learn to effectively distinguish wordplay from non-wordplay, and not simply texts differing in length, style, or vocabulary. Our test collection represents then a step towards wordplay-aware multilingual information retrieval.