Hierarchical Sketch Induction for Paraphrase Generation

ACL 2022  ·  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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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Paraphrase Generation MSCOCO HRQ-VAE iBLEU 19.04 # 1
BLEU 27.90 # 1
Paraphrase Generation Paralex HRQ-VAE iBLEU 24.93 # 1
BLEU 39.49 # 1
Paraphrase Generation Quora Question Pairs HRQ-VAE iBLEU 18.42 # 1
BLEU 33.11 # 1


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