论文标题
释义生成的分层草图归纳
Hierarchical Sketch Induction for Paraphrase Generation
论文作者
论文摘要
我们提出了一种释义生成的生成模型，该模型通过在显式句法草图上调节来鼓励句法多样性。我们介绍了分层细化量化的变分自动编码器（HRQ-VAE），这是一种学习致密编码分解的方法，作为一系列离散的潜在变量，使粒度增加的迭代精炼。代码的层次结构是通过端到端培训来学习的，并代表了有关输入的精细到谷物的信息。我们使用HRQ-VAE将输入句子的句法形式编码为层次结构的路径，从而使我们可以在测试时间更容易预测句法草图。包括人类评估在内的广泛实验证实，HRQ-VAE学习了输入空间的层次表示，并产生比以前系统更高质量的释义。
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.
