UR-FUNNY: A Multimodal Language Dataset for Understanding Humor
Table 4: Binary accuracy for different variants of C-MFN and training scenarios outlined in Section 5. The best performance is achieved using all three modalities of text (T), vision (V) and acoustic (A).
['Modality', 'T', 'A+V', 'T+A', 'T+V', 'T+A+V']
Results demonstrate that both context and punchline information are important as C-MFN outperforms C-MFN (P) and C-MFN (C) models. Punchline is the most important component for detecting humor as the performance of C-MFN (P) is significantly higher than C-MFN (C). Therefore, UR-FUNNY dataset presents new challenges to the field of NLP, specifically research areas of humor detection and multimodal language analysis.