Rˆ3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge (2020.acl-main)
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| Challenge: | Existing work on sarcasm generation focuses on context incongruity, but new work addresses this problem . |
| Approach: | They propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence. |
| Outcome: | The proposed method generates sarcasm better than humans 34% of the time and better than a reinforced hybrid baseline 90% of the times. |
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| Challenge: | Existing methods for sarcasm detection are limited by supervised learning or prompt engineering . a new approach decomposes sarcasm detection into three dimensions: language, context, and emotion . |
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| Challenge: | sarcasm detection datasets focus on intended, rather than perceived sarcasm, but there is no comparison between human and machine performance. |
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