2,057 research outputs found
Data Augmentation for Spoken Language Understanding via Joint Variational Generation
Data scarcity is one of the main obstacles of domain adaptation in spoken
language understanding (SLU) due to the high cost of creating manually tagged
SLU datasets. Recent works in neural text generative models, particularly
latent variable models such as variational autoencoder (VAE), have shown
promising results in regards to generating plausible and natural sentences. In
this paper, we propose a novel generative architecture which leverages the
generative power of latent variable models to jointly synthesize fully
annotated utterances. Our experiments show that existing SLU models trained on
the additional synthetic examples achieve performance gains. Our approach not
only helps alleviate the data scarcity issue in the SLU task for many datasets
but also indiscriminately improves language understanding performances for
various SLU models, supported by extensive experiments and rigorous statistical
testing.Comment: 8 pages, 3 figures, 4 tables, Accepted in AAAI201
Deep AutoRegressive Networks
We introduce a deep, generative autoencoder capable of learning hierarchies
of distributed representations from data. Successive deep stochastic hidden
layers are equipped with autoregressive connections, which enable the model to
be sampled from quickly and exactly via ancestral sampling. We derive an
efficient approximate parameter estimation method based on the minimum
description length (MDL) principle, which can be seen as maximising a
variational lower bound on the log-likelihood, with a feedforward neural
network implementing approximate inference. We demonstrate state-of-the-art
generative performance on a number of classic data sets: several UCI data sets,
MNIST and Atari 2600 games.Comment: Appears in Proceedings of the 31st International Conference on
Machine Learning (ICML), Beijing, China, 201
- …