Abstract
The JOKER Lab at the Conference and Labs of the Evaluation Forum (CLEF) aims to foster research on automated processing of verbal humour, including tasks such as retrieval, classification, interpretation, generation, and translation. Despite the heady success of large language models, humour and wordplay automatic processing are far from being a solved problem. JOKER brings together experts from the social and computational sciences and encourages them to collaborate on shared tasks with quality-controlled annotated datasets. In 2024, we will offer entirely new shared tasks on humour-aware information retrieval, as well as fine-grained sentiment analysis and classification of humour for conversational agents. As in the past JOKER Labs, we will also make our data available for an unshared task that solicits novel use cases. In this paper, we provide a brief retrospective on the JOKER Labs, with a focus on the results and lessons learnt from last year’s iteration, and we preview the tasks to be held at JOKER 2024.
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Notes
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In a shared task, the organisers establish the criteria for assessing an unresolved artificial intelligence issue and create a dataset that has been annotated by humans for training and testing. Participants in the challenge then use the publicly available training data to create solutions for addressing the problem. Subsequently, the organisers assess these solutions using a private, undisclosed test dataset. In an unshared task, the organisers offer annotated data without specifying a specific problem to solve. Participants are encouraged to leverage this data to propose and address new, unique problems of their choosing.
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The classification proposed in the related past work [18] was intended to classify playful requests to the Amazon Alexa virtual assistant.
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Acknowledgments
This project has received a government grant managed by the National Research Agency under the program “Investissements d’avenir” integrated into France 2030, with the Reference ANR-19-GURE-0001. JOKER is supported by La Maison des sciences de l’homme en Bretagne. This Lab would not have been possible without the great support of numerous individuals; we would like to thank in particular the students of the Université de Bretagne Occidentale for their contribution to data construction.
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Ermakova, L. et al. (2024). CLEF 2024 JOKER Lab: Automatic Humour Analysis. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14613. Springer, Cham. https://doi.org/10.1007/978-3-031-56072-9_5
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