@inproceedings{weller-seppi-2019-humor,
title = "Humor Detection: A Transformer Gets the Last Laugh",
author = "Weller, Orion and
Seppi, Kevin",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1372",
doi = "10.18653/v1/D19-1372",
pages = "3621--3625",
abstract = "Much previous work has been done in attempting to identify humor in text. In this paper we extend that capability by proposing a new task: assessing whether or not a joke is humorous. We present a novel way of approaching this problem by building a model that learns to identify humorous jokes based on ratings gleaned from Reddit pages, consisting of almost 16,000 labeled instances. Using these ratings to determine the level of humor, we then employ a Transformer architecture for its advantages in learning from sentence context. We demonstrate the effectiveness of this approach and show results that are comparable to human performance. We further demonstrate our model{'}s increased capabilities on humor identification problems, such as the previously created datasets for short jokes and puns. These experiments show that this method outperforms all previous work done on these tasks, with an F-measure of 93.1{\%} for the Puns dataset and 98.6{\%} on the Short Jokes dataset.",
}
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%0 Conference Proceedings
%T Humor Detection: A Transformer Gets the Last Laugh
%A Weller, Orion
%A Seppi, Kevin
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F weller-seppi-2019-humor
%X Much previous work has been done in attempting to identify humor in text. In this paper we extend that capability by proposing a new task: assessing whether or not a joke is humorous. We present a novel way of approaching this problem by building a model that learns to identify humorous jokes based on ratings gleaned from Reddit pages, consisting of almost 16,000 labeled instances. Using these ratings to determine the level of humor, we then employ a Transformer architecture for its advantages in learning from sentence context. We demonstrate the effectiveness of this approach and show results that are comparable to human performance. We further demonstrate our model’s increased capabilities on humor identification problems, such as the previously created datasets for short jokes and puns. These experiments show that this method outperforms all previous work done on these tasks, with an F-measure of 93.1% for the Puns dataset and 98.6% on the Short Jokes dataset.
%R 10.18653/v1/D19-1372
%U https://aclanthology.org/D19-1372
%U https://doi.org/10.18653/v1/D19-1372
%P 3621-3625
Markdown (Informal)
[Humor Detection: A Transformer Gets the Last Laugh](https://aclanthology.org/D19-1372) (Weller & Seppi, EMNLP-IJCNLP 2019)
ACL
- Orion Weller and Kevin Seppi. 2019. Humor Detection: A Transformer Gets the Last Laugh. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3621–3625, Hong Kong, China. Association for Computational Linguistics.