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.
@article{arxiv.2210.13985,
title = {This joke is [MASK]: Recognizing Humor and Offense with Prompting},
author = {Junze Li and Mengjie Zhao and Yubo Xie and Antonis Maronikolakis and Pearl Pu and Hinrich Schütze},
journal= {arXiv preprint arXiv:2210.13985},
year = {2022}
}
Comments
Transfer Learning for Natural Language Processing Workshop at NeurIPS 2022