论文标题
PDD的几种自然语言推理产生：提示和动态演示
Few-Shot Natural Language Inference Generation with PDD: Prompt and Dynamic Demonstration
论文作者
论文摘要
自然语言推理的生成任务是给定文本前提和两者之间的逻辑关系产生文本假设。在实践中，此任务可用于数据增强和可控制的文本生成。在本文中，我们提出了具有迅速和动态演示（LM-PDD）的语言模型，以在几次播放设置中解决此问题。我们的框架的表现优于资源低的标准微调模型，在SNLI和MNLI数据集上平均实现了8％的绝对改进，而13个自然语言分类任务的结果也表明我们的动态演示方法具有良好的推广性。
Natural Language Inference Generation task is to generate a text hypothesis given a text premise and a logical relation between the two. This task can be used in data augmentation and controllable text generation in practice. In this paper, we propose language models with prompt and dynamic demonstration (LM-PDD) to tackle this problem in few-shot settings. Our framework outperforms standard fine-tuned models with low resource, achieving an average 8% absolute improvement on SNLI and MNLI datasets, and the results on 13 natural language classification tasks also show that our dynamic demonstration method has good generalizability.
