“句式结构是一种基于句本位语法的形式化句法结构,采用自定义的图解形式呈现句子结构。本文提出了涵盖小句结构、词法结构和句间结构三方面的句式结构体系,阐明了其设计理念以及句本位的析句原则,最后概述了基于该体系构建汉语树库的工程进展情况。”

Variational autoencoder (VAE) is a widely used generative model that gains great popularity for its capability in density estimation and representation learning. However, when employing a strong autoregressive generation network, VAE tends to converge to a degenerate local optimum known as posterior collapse. In this paper, we propose a model named Scale-VAE to solve this problem. Scale-VAE does not force the KL term to be larger than a positive constant, but aims to make the latent variables easier to be exploited by the generation network. Specifically, each dimension of the mean for the approximate posterior distribution is multiplied by a factor to keep that dimension discriminative across data instances. The same factors are used for all data instances so as not to change the relative relationship between the posterior distributions. Latent variables from the scaled-up posteriors are fed into the generation network, but the original posteriors are still used to calculate the KL term. In this way, Scale-VAE can solve the posterior collapse problem with a training cost similar to or even lower than the basic VAE. Experimental results show that Scale-VAE outperforms state-of-the-art models in density estimation, representation learning, and consistency of the latent space, and is competitive with other models in generation.