Unsupervised Paraphrasing with Pretrained Language Models

TL;DR

This paper introduces an unsupervised method for paraphrase generation using pre-trained language models, employing task-adaptation, self-supervision, and a novel decoding algorithm called Dynamic Blocking to achieve state-of-the-art results.

Contribution

The authors propose a new unsupervised training pipeline with Dynamic Blocking, enabling high-quality paraphrasing without labeled data, and demonstrate its effectiveness across datasets and languages.

Findings

Achieves state-of-the-art performance on QQP and ParaNMT datasets.

Robust to domain shifts between datasets.

Transfers effectively to other languages without fine-tuning.

Abstract

Paraphrase generation has benefited extensively from recent progress in the designing of training objectives and model architectures. However, previous explorations have largely focused on supervised methods, which require a large amount of labeled data that is costly to collect. To address this drawback, we adopt a transfer learning approach and propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking (DB). To enforce a surface form dissimilar from the input, whenever the language model emits a token contained in the source sequence, DB prevents the model from outputting the subsequent source token for the next generation step. We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair (QQP) and the ParaNMT datasets and is robust to domain shift between the two datasets of distinct distributions. We also demonstrate that our model transfers to paraphrasing in other languages without any additional finetuning.