@inproceedings{hosking-etal-2022-hierarchical,
title = "Hierarchical Sketch Induction for Paraphrase Generation",
author = "Hosking, Tom and
Tang, Hao and
Lapata, Mirella",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.178",
doi = "10.18653/v1/2022.acl-long.178",
pages = "2489--2501",
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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%0 Conference Proceedings
%T Hierarchical Sketch Induction for Paraphrase Generation
%A Hosking, Tom
%A Tang, Hao
%A Lapata, Mirella
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F hosking-etal-2022-hierarchical
%X 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.
%R 10.18653/v1/2022.acl-long.178
%U https://aclanthology.org/2022.acl-long.178
%U https://doi.org/10.18653/v1/2022.acl-long.178
%P 2489-2501
Markdown (Informal)
[Hierarchical Sketch Induction for Paraphrase Generation](https://aclanthology.org/2022.acl-long.178) (Hosking et al., ACL 2022)
ACL
- Tom Hosking, Hao Tang, and Mirella Lapata. 2022. Hierarchical Sketch Induction for Paraphrase Generation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2489–2501, Dublin, Ireland. Association for Computational Linguistics.