@inproceedings{lee-etal-2016-make,
title = "Can We Make Computers Laugh at Talks?",
author = "Lee, Chong Min and
Yoon, Su-Youn and
Chen, Lei",
editor = "Nissim, Malvina and
Patti, Viviana and
Plank, Barbara",
booktitle = "Proceedings of the Workshop on Computational Modeling of People`s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/W16-4319/",
pages = "173--181",
abstract = "Considering the importance of public speech skills, a system which makes a prediction on where audiences laugh in a talk can be helpful to a person who prepares for a talk. We investigated a possibility that a state-of-the-art humor recognition system can be used in detecting sentences inducing laughters in talks. In this study, we used TED talks and laughters in the talks as data. Our results showed that the state-of-the-art system needs to be improved in order to be used in a practical application. In addition, our analysis showed that classifying humorous sentences in talks is very challenging due to close distance between humorous and non-humorous sentences."
}
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%0 Conference Proceedings
%T Can We Make Computers Laugh at Talks?
%A Lee, Chong Min
%A Yoon, Su-Youn
%A Chen, Lei
%Y Nissim, Malvina
%Y Patti, Viviana
%Y Plank, Barbara
%S Proceedings of the Workshop on Computational Modeling of People‘s Opinions, Personality, and Emotions in Social Media (PEOPLES)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F lee-etal-2016-make
%X Considering the importance of public speech skills, a system which makes a prediction on where audiences laugh in a talk can be helpful to a person who prepares for a talk. We investigated a possibility that a state-of-the-art humor recognition system can be used in detecting sentences inducing laughters in talks. In this study, we used TED talks and laughters in the talks as data. Our results showed that the state-of-the-art system needs to be improved in order to be used in a practical application. In addition, our analysis showed that classifying humorous sentences in talks is very challenging due to close distance between humorous and non-humorous sentences.
%U https://aclanthology.org/W16-4319/
%P 173-181
Markdown (Informal)
[Can We Make Computers Laugh at Talks?](https://aclanthology.org/W16-4319/) (Lee et al., PEOPLES 2016)
ACL
- Chong Min Lee, Su-Youn Yoon, and Lei Chen. 2016. Can We Make Computers Laugh at Talks?. In Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES), pages 173–181, Osaka, Japan. The COLING 2016 Organizing Committee.