UR-FUNNY: A Multimodal Language Dataset for Understanding Humor
Table 1: Comparison between UR-FUNNY and notable humor detection datasets in the NLP community. Here, ‘pos’, ’neg’ , ‘mod’ and ‘spk’ denote positive, negative, modalities and speaker respectively.
['Dataset', '#Pos', '#Neg', 'Mod', 'type', '#spk']
Humor Analysis: Humor analysis has been among active areas of research in both natural language processing and affective computing. Pun of the Day” Yang et al. The above datasets have studied humor from different perspectives. For example, “16000 One-Liner” and “Pun of the Day” focus on joke detection (joke vs. not joke binary task), while “Ted Laughter” focuses on punchline detection (whether or not punchline triggers laughter). Similar to “Ted Laughter”, UR-FUNNY focuses on punchline detection. Furthermore, punchline is accompanied by context sentences to properly model the build up of humor. Unlike previous datasets where negative samples were drawn from a different domain, UR-FUNNY uses a challenging negative sampling case where samples are drawn from the same videos. Furthermore, UR-FUNNY is the only humor detection dataset which incorporates all three modalities of text, vision and audio.