Language Modeling with Gated Convolutional Networks

[flrngel]

https://arxiv.org/abs/1612.08083

Abstract

• propose gating mechanism
• uses WikiText-103 and Google Billion Words
• proposed model is very competitive to strong recurrent models on large scale language tasks

1. Introduction

• convolutional network has parallelization benefit
  • but cuDNN is not optimized for 1d convolutions yet
• Gated linear solves vanishing gradient
• Compare to PixcelCNN, Oord et al. 2016, GLU is better than LSTM-style gating (GTU)

2. Approach

• convolutional has no temporal dependency compare to recurrent models
• recurrent models have infinite contexts but paper's experiment shows it is not necessary
• GLU
• abstract model
• model uses adaptive softmax which assign higher capacity to very frequent words and lower capacity to rare words
  • this results faster coputation and needs lower memory

3. Gating Mechanisms

• purpose of gating mechanism is to control what information should be propagated through the hierarchy of layers
• comparing to GTU (LSTM-style gating mechanism), gradient of GTU gradually vanishes because of downscaling factors tanh'(X) and \sigma'(X) but GLU doesn't have downscaling factor
  • this can be thought of as a multiplicative skip connection (which helps gradients flow through the layers)

4. Experimental Setup

4.2. Training

• uses gradient clipping on training and it works well

4.3. Hyper-parameters

• initialize layers with Kaiming initialization

5. Results

5.3. Non-linear Modeling

• Bilinear layers + GLU performs best

TODO

• read http://proceedings.mlr.press/v9/gutmann10a/gutmann10a.pdf