Quality Controlled Paraphrase Generation

TL;DR

This paper introduces QCPG, a novel model for controlling the quality of paraphrase generation, enabling the creation of diverse, high-quality paraphrases that preserve original meaning, with demonstrated improvements over baseline methods.

Contribution

It proposes a new quality-guided control method for paraphrase generation and a technique to identify optimal quality points for better paraphrases.

Findings

Generated paraphrases maintain meaning while increasing diversity.

QCPG outperforms uncontrolled baselines in quality and diversity.

The approach offers scalable and flexible quality control in paraphrase generation.

Abstract

Paraphrase generation has been widely used in various downstream tasks. Most tasks benefit mainly from high quality paraphrases, namely those that are semantically similar to, yet linguistically diverse from, the original sentence. Generating high-quality paraphrases is challenging as it becomes increasingly hard to preserve meaning as linguistic diversity increases. Recent works achieve nice results by controlling specific aspects of the paraphrase, such as its syntactic tree. However, they do not allow to directly control the quality of the generated paraphrase, and suffer from low flexibility and scalability. Here we propose $QCPG$, a quality-guided controlled paraphrase generation model, that allows directly controlling the quality dimensions. Furthermore, we suggest a method that given a sentence, identifies points in the quality control space that are expected to yield optimal generated paraphrases. We show that our method is able to generate paraphrases which maintain the original meaning while achieving higher diversity than the uncontrolled baseline. The models, the code, and the data can be found in https://github.com/IBM/quality-controlled-paraphrase-generation.