Predicting Audience's Laughter Using Convolutional Neural Network
This work addresses humor recognition for automated speaker evaluation, but it is incremental as it applies an existing CNN method to a new domain-specific dataset.
The paper tackled the problem of automatically evaluating humor usage by speakers by building a presentation corpus from TED talks and comparing a Convolutional Neural Network (CNN) method with a conventional linguistic approach for humor recognition. The result showed that the CNN method achieved higher detection accuracies and learned essential features automatically.
For the purpose of automatically evaluating speakers' humor usage, we build a presentation corpus containing humorous utterances based on TED talks. Compared to previous data resources supporting humor recognition research, ours has several advantages, including (a) both positive and negative instances coming from a homogeneous data set, (b) containing a large number of speakers, and (c) being open. Focusing on using lexical cues for humor recognition, we systematically compare a newly emerging text classification method based on Convolutional Neural Networks (CNNs) with a well-established conventional method using linguistic knowledge. The advantages of the CNN method are both getting higher detection accuracies and being able to learn essential features automatically.