Deep Learning of Audio and Language Features for Humor Prediction
Dario Bertero, Pascale Fung · 2016
We propose a comparison between various supervised machine learning methods to predict and detect humor in dialogues.We retrieve our humorous dialogues from a very popular TV sitcom: "The Big Bang Theory".We build a corpus where punchlines are annotated using the canned laughter embedded in the audio track.Our comparative study involves a linear-chain Conditional Random Field over a Recurrent Neural Network and a Convolutional Neural Network.Using a combination of word-level and audio frame-level features, the CNN outperforms the other methods, obtaining the best F-score of 68.5% over 66.5% by CRF and 52.9% by RNN.Our work is a starting point to developing more effective machine learning and neural network models on the humor prediction task, as well as developing machines capable in understanding humor in general.