Novelty Controlled Paraphrase Generation with Retrieval Augmented Conditional Prompt Tuning

• URL: http://arxiv.org/abs/2202.00535v1
• Date: Tue, 1 Feb 2022 16:26:36 GMT
• Title: Novelty Controlled Paraphrase Generation with Retrieval Augmented Conditional Prompt Tuning
• Authors: Jishnu Ray Chowdhury, Yong Zhuang, Shuyi Wang
• Abstract summary: Paraphrase generation is a fundamental and long-standing task in natural language processing. We propose Retrieval Augmented Prompt Tuning (RAPT) as a parameter-efficient method to adapt large pre-trained language models for paraphrase generation. We also propose Novelty Conditioned RAPT (NC-RAPT) as a simple model-agnostic method of using specialized prompt tokens for controlled paraphrase generation.
• Score: 8.142947808507367
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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