Abstract
Computational humor has been a fascinating topic that poses great challenge to artificial intelligence. For computers to understand and tell jokes does not seem to be an trivial task that remains to be a mystery. There have been very few attempts in literature that discuss how to build computational models in either discovering the structures of hu- mor, recognizing humor or even generating humor. In this thesis, I construct and collect four datasets with distinct joke types in both English and Chinese and conduct learn- ing experiments on humor recognition. I implement a Convolutional Neural Network (CNN) with extensive filter size, number and Highway Networks to increase the depth of networks. Results show that our model outperforms in recognition of different types of humor with benchmarks collected in both English and Chinese languages on accu- racy, precision, and recall in comparison to previous works. In addition to recognition, we also conduct research on humor generation that utilize adversarial networks combine with reinforcement learning (policy gradient) to generate humorous text. We purpose a two discriminators architecture that indicate more precisely rewards for generator to improve learning and produce quality jokes.