A Neural Approach to Irony Generation
Ironies can not only express stronger emotions but also show a sense of humor. With the development of social media, ironies are widely used in public. Although many prior research studies have been conducted in irony detection, few studies focus on irony generation. The main challenges for irony generation are the lack of large-scale irony dataset and difficulties in modeling the ironic pattern. In this work, we first systematically define irony generation based on style transfer task. To address the lack of data, we make use of twitter and build a large-scale dataset. We also design a combination of rewards for reinforcement learning to control the generation of ironic sentences. Experimental results demonstrate the effectiveness of our model in terms of irony accuracy, sentiment preservation, and content preservation.
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Mengdi Zhu (add twitter)
Zhiwei Yu (add twitter)
Xiaojun Wan (add twitter)
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09/15/19 06:03PM
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arxiv_cs_LG: A Neural Approach to Irony Generation. Mengdi Zhu, Zhiwei Yu, and Xiaojun Wan https://t.co/HKT304Qhdk
arxiv_cscl: A Neural Approach to Irony Generation https://t.co/aRFqNBmcX6
arxiv_cscl: A Neural Approach to Irony Generation https://t.co/aRFqNBmcX6
SciFi: A Neural Approach to Irony Generation. https://t.co/QZtgh7h1OG
arxiv_cscl: A Neural Approach to Irony Generation https://t.co/aRFqNBmcX6
arxivml: "A Neural Approach to Irony Generation", Mengdi Zhu, Zhiwei Yu, Xiaojun Wan https://t.co/FMpI9oeB3Z
arxiv_cs_LG: A Neural Approach to Irony Generation. Mengdi Zhu, Zhiwei Yu, and Xiaojun Wan https://t.co/HKT304Qhdk
BrundageBot: A Neural Approach to Irony Generation. Mengdi Zhu, Zhiwei Yu, and Xiaojun Wan https://t.co/jJLZFaBsYA
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