Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation

Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation

This paper explores deep latent variable models for semi-supervised paraphrase generation, where the missing target pair for unlabelled data is modelled as a latent paraphrase sequence. We present a novel unsupervised model named variational sequence auto-encoding reconstruction (VSAR), which perfor...

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Bibliographic Details
Main Authors: Jialin Yu, Alexandra I. Cristea, Anoushka Harit, Zhongtian Sun, Olanrewaju Tahir Aduragba, Lei Shi, Noura Al Moubayed
Format: Article
Language:English
Published: KeAi Communications Co. Ltd. 2023-01-01
Series:AI Open
Subjects:
Deep latent variable models
Paraphrase generation
Semi-supervised learning
Natural language processing
Deep learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2666651023000025

Internet

http://www.sciencedirect.com/science/article/pii/S2666651023000025

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