Abstract
Humor detection attracts increased attention in natural language processing for its potential applications. Prior work focus on analyzing humor on isolated, textual data, but humor usually comes from the interaction among speakers in a multimodal way. In this paper, we proposed a novel dataset named MUMOR, which consists of multimodal dialogues in both English and Chinese. It contains a total of 29,585 utterances belonging to 1,298 dialogues from two TV-sitcoms. We manually annotated each utterance with humor, emotion, and sentiment labels. To our best knowledge, this is the first corpus containing Chinese conversations for humor detection. This dataset could be used for research on humor detection, humor generation, and multi-task learning on emotion and humor analysis. We released this dataset publicly.
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Wu, J., Lin, H., Yang, L., Xu, B. (2021). MUMOR: A Multimodal Dataset for Humor Detection in Conversations. In: Wang, L., Feng, Y., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2021. Lecture Notes in Computer Science(), vol 13028. Springer, Cham. https://doi.org/10.1007/978-3-030-88480-2_49
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