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Stimulating Creativity with FunLines: A Case Study of Humor Generation in Headlines

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arxiv 2002.02031 v1 pith:H23QISAX submitted 2020-02-05 cs.AI cs.CL

classification cs.AIcs.CL
keywords humorfunlinesheadlinesplayersdatadatasetgenerationanalysis
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Building datasets of creative text, such as humor, is quite challenging. We introduce FunLines, a competitive game where players edit news headlines to make them funny, and where they rate the funniness of headlines edited by others. FunLines makes the humor generation process fun, interactive, collaborative, rewarding and educational, keeping players engaged and providing humor data at a very low cost compared to traditional crowdsourcing approaches. FunLines offers useful performance feedback, assisting players in getting better over time at generating and assessing humor, as our analysis shows. This helps to further increase the quality of the generated dataset. We show the effectiveness of this data by training humor classification models that outperform a previous benchmark, and we release this dataset to the public.

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Cited by 1 Pith paper

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  1. A Causality-aware Paradigm for Evaluating Creativity of Multimodal Large Language Models

    cs.AI 2025-01 conditional novelty 6.0 of 10

    LoTbench, an interactive causality-aware benchmark built on Oogiri humor tasks, ranks multimodal LLMs and finds their creativity is moderately below human levels yet strongly correlated with general multimodal cogniti...

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