This Joke Is [MASK]: Recognizing Humor and Offense With Prompting

MCML Authors

Antonis Maronikolakis

* Former Member

→ Group Hinrich Schütze
 Computational Linguistics

Hinrich Schütze

Prof. Dr.

Core PI

Computational Linguistics

Abstract

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.

inproceedings LZX+22

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TL4NLP @NeurIPS 2022

1st Transfer Learning for Natural Language Processing Workshop at the 36th Conference on Neural Information Processing Systems. New Orleans, LA, USA, Nov 28-Dec 09, 2022.

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Authors

J. Li • M. Zhao • Y. Xie • A. Maronikolakis • P. Pu • H. Schütze

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Links

URL

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Research Area

 B2 | Natural Language Processing