论文标题
通过检索增强有条件调整的新颖性控制术产生
Novelty Controlled Paraphrase Generation with Retrieval Augmented Conditional Prompt Tuning
论文作者
论文摘要
释义生成是自然语言处理中的一项基本和长期任务。在本文中，我们集中于对任务的两种贡献：（1）我们提出检索增强及时调整（RAPT）作为一种参数效率高效的方法，以适应大型的预训练的语言模型以生成释义； （2）我们将新颖性调节性RAPT（NC-RAPT）作为一种简单的模型 - 不合Snostic方法，它使用专门的及时令牌来进行受控释义的产生，具有不同水平的词汇新颖性。通过在四个数据集上进行广泛的实验，我们证明了所提出的方法保留原始文本的语义内容的有效性，同时诱导了这一代人的词汇新颖性。
Paraphrase generation is a fundamental and long-standing task in natural language processing. In this paper, we concentrate on two contributions to the task: (1) we propose Retrieval Augmented Prompt Tuning (RAPT) as a parameter-efficient method to adapt large pre-trained language models for paraphrase generation; (2) we propose Novelty Conditioned RAPT (NC-RAPT) as a simple model-agnostic method of using specialized prompt tokens for controlled paraphrase generation with varying levels of lexical novelty. By conducting extensive experiments on four datasets, we demonstrate the effectiveness of the proposed approaches for retaining the semantic content of the original text while inducing lexical novelty in the generation.
