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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.
Original language | English |
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Title of host publication | Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) |
Place of Publication | Dublin, Ireland |
Publisher | Association for Computational Linguistics |
Pages | 2489-2501 |
Number of pages | 13 |
DOIs | |
Publication status | Published - 1 May 2022 |
Event | 60th Annual Meeting of the Association for Computational Linguistics - The Convention Centre Dublin, Dublin, Ireland Duration: 22 May 2022 → 27 May 2022 https://www.2022.aclweb.org |
Conference
Conference | 60th Annual Meeting of the Association for Computational Linguistics |
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Abbreviated title | ACL 2022 |
Country/Territory | Ireland |
City | Dublin |
Period | 22/05/22 → 27/05/22 |
Internet address |
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