Unsupervised Paraphrase Generation using Pre-trained Language Models
By Chaitra V. Hegde, Shrikumar Patil. 2020
43 citations recorded by the source corpus.
Large scale Pre-trained Language Models have proven to be very powerful approach in various Natural language tasks. OpenAI's GPT-2 \cite{radford2019language} is notable for its capability to generate fluent, well formulated, grammatically consistent text and for phrase completions. In this paper we leverage this generation capability of GPT-2 to generate paraphrases without any supervision from labelled data. We examine how the results compare with other supervised and unsupervised approaches and the effect of using paraphrases for data augmentation on downstream tasks such as classification. Our experiments show that paraphrases generated with our model are of good quality, are diverse and improves the downstream task performance when used for data augmentation.
Subfields: Natural Language Processing
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In Natural Language Processing
- Tao Lin
- Heike Adel
- Jannik Strotgen
- Daniel M. Ziegler
- Dietrich Klakow
- Lukas Lange
- Michael A. Hedderich
- Buck Shlegeris
Citations in the corpus
- Adversarial Training for High-Stakes Reliability (2022, methodology): “To increase the size of our adversarial dataset, we used GPT-3 [7] to paraphrase manually generated human adversarial snippets, as has been done with previous generative language models [48].”
- A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios (2020, methodology): “Back-translation has also been leveraged for text classification (Xie et al., 2020; Hegde and Patil, 2020).”