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.- Anthology ID:
- 2022.acl-long.178
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2489–2501
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.178
- DOI:
- 10.18653/v1/2022.acl-long.178
- Cite (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.
- Cite (Informal):
- Hierarchical Sketch Induction for Paraphrase Generation (Hosking et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.acl-long.178.pdf
- Code
- tomhosking/hrq-vae
- Data
- MS COCO, MSCOCO, Paralex, Quora Question Pairs