Prompt Tuning with Contradictory Intentions for Sarcasm Recognition

作者：Yiyi Liu, Ruqing Zhang, Yixing Fan, Jiafeng Guo, Xueqi Cheng · 年份：2023 · DOI：10.18653/v1/2023.eacl-main.25 · 被引用次数：11 · 研究领域：Evolutionary Psychology and Human Behavior、Law in Society and Culture、Geographies of human-animal interactions

Recently, prompt tuning has achieved promising results in a variety of natural language processing (NLP) tasks.The typical approach is to insert text pieces (i.e., templates) into the input and transform downstream tasks into the same form as pre-training.In essence, a high-quality template is the foundation of prompt tuning to support the performance of the converted clozestyle task.However, for sarcasm recognition, it is time-consuming and requires increasingly sophisticated domain knowledge to determine the appropriate templates and label words due to its highly figurative nature.In this work, we propose SarcPrompt, to incorporate the prior knowledge about contradictory intentions into prompt tuning for sarcasm recognition.Sar-cPrompt is inspired by that the speaker usually says the opposite of what they actually mean in the sarcastic text.Based on this idea, we explicitly mimic the actual intention by prompt construction and indicate whether the actual intention is contradictory to the literal content by verbalizer engineering.Experiments on three public datasets with standard and low-resource settings demonstrate the effectiveness of our SarcPrompt for sarcasm recognition.