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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.09203 |
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| _version_ | 1866913609571893248 |
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| author | Vikhorev, Dmitry Galimzianova, Daria Gorovaia, Svetlana Zhemchuzhina, Elizaveta Yamshchikov, Ivan P. |
| author_facet | Vikhorev, Dmitry Galimzianova, Daria Gorovaia, Svetlana Zhemchuzhina, Elizaveta Yamshchikov, Ivan P. |
| contents | Humor generation is a challenging task in natural language processing due to limited resources and the quality of existing datasets. Available humor language resources often suffer from toxicity and duplication, limiting their effectiveness for training robust models. This paper proposes CleanComedy, a specialized, partially annotated toxicity-filtered corpus of English and Russian jokes collected from various sources. We study the effectiveness of our data filtering approach through a survey on humor and toxicity levels in various joke groups. In addition, we study advances in computer humor generation by comparing jokes written by humans with various groups of generative jokes, including our baseline models trained on the CleanComedy datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09203 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CleanComedy: Creating Friendly Humor through Generative Techniques Vikhorev, Dmitry Galimzianova, Daria Gorovaia, Svetlana Zhemchuzhina, Elizaveta Yamshchikov, Ivan P. Computation and Language Humor generation is a challenging task in natural language processing due to limited resources and the quality of existing datasets. Available humor language resources often suffer from toxicity and duplication, limiting their effectiveness for training robust models. This paper proposes CleanComedy, a specialized, partially annotated toxicity-filtered corpus of English and Russian jokes collected from various sources. We study the effectiveness of our data filtering approach through a survey on humor and toxicity levels in various joke groups. In addition, we study advances in computer humor generation by comparing jokes written by humans with various groups of generative jokes, including our baseline models trained on the CleanComedy datasets. |
| title | CleanComedy: Creating Friendly Humor through Generative Techniques |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.09203 |