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Humor recognition in conversations is a challenging task that has recentlygained popularity due to its importance in dialogue understanding, including inmultimodal settings (i.e., text, acoustics, and visual). The few existingdatasets for humor are mostly in English. However, due to the tremendous growthin multilingual content, there is a great demand to build models and systemsthat support multilingual information access. To this end, we propose a datasetfor Multimodal Multiparty Hindi Humor (M2H2) recognition in conversationscontaining 6,191 utterances from 13 episodes of a very popular TV series"Shrimaan Shrimati Phir Se". Each utterance is annotated with humor/non-humorlabels and encompasses acoustic, visual, and textual modalities. We proposeseveral strong multimodal baselines and show the importance of contextual andmultimodal information for humor recognition in conversations. The empiricalresults on M2H2 dataset demonstrate that multimodal information complementsunimodal information for humor recognition. The dataset and the baselines areavailable at http://www.iitp.ac.in/~ai-nlp-ml/resources.html andhttps://github.com/declare-lab/M2H2-dataset.

M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations