@inproceedings{L16-1079,
 abstract = {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.
},
 address = {Portorož, Slovenia},
 author = {Dario Bertero and Pascale Fung},
 booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
 month = {May},
 pages = {496--501},
 publisher = {European Language Resources Association (ELRA)},
 title = {Deep Learning of Audio and Language Features for Humor Prediction},
 url = {https://www.aclweb.org/anthology/L16-1079},
 year = {2016}
}