Hierarchical Sketch Induction for Paraphrase Generation

• URL: http://arxiv.org/abs/2203.03463v1
• Date: Mon, 7 Mar 2022 15:28:36 GMT
• Title: Hierarchical Sketch Induction for Paraphrase Generation
• Authors: Tom Hosking, Hao Tang, Mirella Lapata
• Abstract summary: We introduce Hierarchical Refinement Quantized Variational Autoencoders (HRQ-VAE), a method for learning decompositions of dense encodings. We use HRQ-VAE to encode the syntactic form of an input sentence as a path through the hierarchy, allowing us to more easily predict syntactic sketches at test time.
• Score: 79.87892048285819
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a generative model of paraphrase generation, that encourages syntactic diversity by conditioning on an explicit syntactic sketch. We introduce Hierarchical Refinement Quantized Variational Autoencoders (HRQ-VAE), a method for learning decompositions of dense encodings as a sequence of discrete latent variables that make iterative refinements of increasing granularity. This hierarchy of codes is learned through end-to-end training, and represents fine-to-coarse grained information about the input. We use HRQ-VAE to encode the syntactic form of an input sentence as a path through the hierarchy, allowing us to more easily predict syntactic sketches at test time. Extensive experiments, including a human evaluation, confirm that HRQ-VAE learns a hierarchical representation of the input space, and generates paraphrases of higher quality than previous systems.

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