Peng-Yu Chen


Humor Recognition Using Deep Learning
Peng-Yu Chen | Von-Wun Soo
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Humor is an essential but most fascinating element in personal communication. How to build computational models to discover the structures of humor, recognize humor and even generate humor remains a challenge and there have been yet few attempts on it. In this paper, we construct and collect four datasets with distinct joke types in both English and Chinese and conduct learning experiments on humor recognition. We 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 accuracy, precision, and recall in comparison to previous works.


Linggle Knows: A Search Engine Tells How People Write
Jhih-Jie Chen | Hao-Chun Peng | Mei-Cih Yeh | Peng-Yu Chen | Jason Chang
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

This paper shows the great potential of incorporating different approaches to help writing. Not only did they solve different kinds of writing problems, but also they complement and reinforce each other to be a complete and effective solution. Despite the extensive and multifaceted feedback and suggestion, writing is not all about syntactically or lexically well-written. It involves contents, structure, the certain understanding of the background, and many other factors to compose a rich, organized and sophisticated text. (e.g., conventional structure and idioms in academic writing). There is still a long way to go to accomplish the ultimate goal. We envision the future of writing to be a joyful experience with the help of instantaneous suggestion and constructive feedback.