  Humor recognition in conversations is a challenging task that has recently
gained popularity due to its importance in dialogue understanding, including in
multimodal settings (i.e., text, acoustics, and visual). The few existing
datasets for humor are mostly in English. However, due to the tremendous growth
in multilingual content, there is a great demand to build models and systems
that support multilingual information access. To this end, we propose a dataset
for Multimodal Multiparty Hindi Humor (M2H2) recognition in conversations
containing 6,191 utterances from 13 episodes of a very popular TV series
"Shrimaan Shrimati Phir Se". Each utterance is annotated with humor/non-humor
labels and encompasses acoustic, visual, and textual modalities. We propose
several strong multimodal baselines and show the importance of contextual and
multimodal information for humor recognition in conversations. The empirical
results on M2H2 dataset demonstrate that multimodal information complements
unimodal information for humor recognition. The dataset and the baselines are
available at http://www.iitp.ac.in/~ai-nlp-ml/resources.html and
https://github.com/declare-lab/M2H2-dataset.
