论文标题
这个笑话是[面具]：通过提示认识到幽默和冒犯
This joke is [MASK]: Recognizing Humor and Offense with Prompting
论文作者
论文摘要
幽默是日常人类互动和通信中的磁成分。计算建模幽默使NLP系统能够娱乐和与用户互动。我们调查了提示的有效性，即NLP的新转移学习范式，以供幽默识别。我们表明，当有许多注释可用时，提示性能与FineTuning类似，但在低资源幽默识别方面具有出色的表现。还通过将影响功能应用于提示来检查幽默与犯罪之间的关系；我们表明，模型可以依靠进攻来确定转移期间的幽默。
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.
