Abstract / Description of output
We introduce Chandler, a system that generates sarcastic responses to a given utterance. Previous sarcasm generators assume the intended meaning that sarcasm conceals is the opposite of the literal meaning. We argue that this traditional theory of sarcasm provides a grounding that is neither necessary, nor sufficient, for sarcasm to occur. Instead, we ground our generation process on a formal theory that specifies conditions that unambiguously differentiate sarcasm from non-sarcasm. Furthermore, Chandler not only generates sarcastic responses, but also explanations for why each response is sarcastic. This provides accountability, crucial for avoiding miscommunication between humans and conversational agents, particularly considering that sarcastic communication can be offensive. In human evaluation, Chandler achieves comparable or higher sarcasm scores, compared to state-of-the-art generators, while generating more diverse responses, that are more specific and more coherent to the input.
Original language | English |
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Title of host publication | Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations |
Place of Publication | Stroudsburg, PA |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 339-349 |
Number of pages | 11 |
ISBN (Electronic) | 978-1-955917-11-7 |
DOIs | |
Publication status | Published - 7 Nov 2021 |
Event | 2021 Conference on Empirical Methods in Natural Language Processing - Punta Cana, Dominican Republic Duration: 7 Nov 2021 → 11 Nov 2021 https://2021.emnlp.org/ |
Conference
Conference | 2021 Conference on Empirical Methods in Natural Language Processing |
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Abbreviated title | EMNLP 2021 |
Country/Territory | Dominican Republic |
City | Punta Cana |
Period | 7/11/21 → 11/11/21 |
Internet address |