Piecewise Latent Variables for Neural Variational Text Processing
Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variables, such as variational autoencoders. The hope is that such models will learn to represent rich, multi-modal latent factors in real-world data, such as natural language text. However, current models often assume simplistic priors on the latent variables - such as the uni-modal Gaussian distribution - which are incapable of representing complex latent factors efficiently. To overcome this restriction, we propose the simple, but highly flexible, piecewise constant distribution. This distribution has the capacity to represent an exponential number of modes of a latent target distribution, while remaining mathematically tractable. Our results demonstrate that incorporating this new latent distribution into different models yields substantial improvements in natural language processing tasks such as document modeling and natural language generation for dialogue.
Code (2)
Tasks
Text GenerationVariational InferenceSimilar Papers 제목 키워드 기반
Identifiability of Potentially Degenerate Gaussian Mixture Models With Piecewise Affine Mixing
Causal representation learning (CRL) aims to identify the underlying latent variables from high-dimensional observations, even when variables are dependent with each other. We study this problem for latent variables that…
Representation LearningNeural Variational Inference for Text Processing
Recent advances in neural variational inference have spawned a renaissance in deep latent variable models. In this paper we introduce a generic variational inference framework for generative and conditional models of tex…
Answer SelectionQuestion AnsweringTopic ModelsVariational InferenceSelf-supervised Latent Space Optimization with Nebula Variational Coding
Deep learning approaches process data in a layer-by-layer way with intermediate (or latent) features. We aim at designing a general solution to optimize the latent manifolds to improve the performance on classification, …
FormMetric LearningVariational InferenceFuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text Generation
The past several years have witnessed Variational Auto-Encoder's superiority in various text generation tasks. However, due to the sequential nature of the text, auto-regressive decoders tend to ignore latent variables a…
DiversityText GenerationBetter Exploiting Latent Variables in Text Modeling
We show that sampling latent variables multiple times at a gradient step helps in improving a variational autoencoder and propose a simple and effective method to better exploit these latent variables through hidden stat…