This joke is [MASK]: Recognizing Humor and Offense with Prompting
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Abstract (arXiv)
Humor is a magnetic component in everyday human interactions and communications. Computationally modeling humor enables NLP systems to entertain and engage with users. We investigate the effectiveness of prompting, a new transfer learning paradigm for NLP, for humor recognition. We show that prompting performs similarly to finetuning when numerous annotations are available, but gives stellar performance in low-resource humor recognition. The relationship between humor and offense is also inspected by applying influence functions to prompting; we show that models could rely on offense to determine humor during transfer.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 43 references that are in the map. First posted on arXiv 25 Oct 2022.
Builds on
17 of 43 references.
References · 17
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- 2020Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functionscited by 144
5 more
- 2021Discrete and Soft Prompting for Multilingual Modelscited by 49
- 2019Humor Detection: A Transformer Gets the Last Laughcited by 46
- 2020Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognitioncited by 13
- 2021LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Frameworkcited by 8
- 2017Predicting Audience's Laughter Using Convolutional Neural Networkcited by 4
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