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Through the Words of Viewers: Using Comment-Content Entangled Network for Humor Impression Recognition
Abstract
Research into understanding humor has been investigated over centuries. It has recently attracted various technical effort in computing humor automatically from data, especially for humor in speech. Comprehension on the same speech and the ability to realize a humor event vary depending on each individual audience's background and experience. Most previous works on automatic humor detection or impression recognition mainly model the produced textual content only without considering audience responses. We collect a corpus of TED Talks including audience comments for each of the presented TED speech. We propose a novel network architecture that considers the natural entanglement between speech transcripts and user's online feedbacks as an integrative graph structure, where the content speech and online feedbacks are nodes where the edges are connected though their common words. Our model achieves 61.2% of accuracy in a threeclass classification on humor impression recognition on TED talks; our experiments further demonstrate viewers comments are essential in improving the recognition tasks, and a joint content-comment modeling achieves the best recognition.
Figures
The schematic of our proposed framework. We first build the content graph and comment graph. A graph includes document nodes, word nodes, and edges between nodes.
The schematic of our proposed framework. We first build the content graph and comment graph. A graph includes document nodes, word nodes, and edges between nodes.
The confusion matrix of the classification result using TextGCN(Union).
The confusion matrix of the classification result using TextGCN(Union).
Keywords
viewer comments | humor recognition | entangled network | speech content
Authors
Huan-Yu Chen Yun-Shao Lin Chi-Chun Lee
Publication Date
2021/01/19
Conference
SLT
2021 IEEE Spoken Language Technology Workshop (SLT)
DOI
10.1109/slt48900.2021.9383564
Publisher
IEEE