论文标题
探索以少数拍摄和零拍设置的委婉检测
Exploring Euphemism Detection in Few-Shot and Zero-Shot Settings
论文作者
论文摘要
这项工作建立在EMNLP 2022 FIGLANG研讨会中提出的委婉语检测基础上，并将其扩展到少量和零拍设置。我们使用共享任务中的数据集演示了几次射击和零射击公式，并且我们使用Roberta和GPT-3在这些设置中进行实验。我们的结果表明，语言模型即使在培训期间看不见的新术语中，语言模型也可以对委婉的术语进行分类，这表明它能够捕获与委婉语有关的更高级别概念。
This work builds upon the Euphemism Detection Shared Task proposed in the EMNLP 2022 FigLang Workshop, and extends it to few-shot and zero-shot settings. We demonstrate a few-shot and zero-shot formulation using the dataset from the shared task, and we conduct experiments in these settings using RoBERTa and GPT-3. Our results show that language models are able to classify euphemistic terms relatively well even on new terms unseen during training, indicating that it is able to capture higher-level concepts related to euphemisms.
