Papers › Hierarchical Sketch Induction for Paraphrase Generation
Hierarchical Sketch Induction for Paraphrase Generation
Tom Hosking, Hao Tang, Mirella Lapata
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Paraphrase Generation | MSCOCO | HRQ-VAE | BLEU | 27.90 | #1 of 1 | Archive leaderboard | report |
| Paraphrase Generation | MSCOCO | HRQ-VAE | iBLEU | 19.04 | #1 of 1 | Archive leaderboard | report |
| Paraphrase Generation | Paralex | HRQ-VAE | BLEU | 39.49 | #1 of 2 | Archive leaderboard | report |
| Paraphrase Generation | Paralex | HRQ-VAE | iBLEU | 24.93 | #1 of 2 | Archive leaderboard | report |
| Paraphrase Generation | Quora Question Pairs | HRQ-VAE | BLEU | 33.11 | #1 of 2 | Archive leaderboard | report |
| Paraphrase Generation | Quora Question Pairs | HRQ-VAE | iBLEU | 18.42 | #1 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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