# Review: Language Modeling with Gated Convolutional Networks (GCNN/GLU)

## Gated Convolutional Networks (GCNN) Using Gated Linear Unit (GLU)

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In this story, **Language Modeling with Gated Convolutional Networks**, (GCNN/GLU), by Facebook AI Research, is briefly reviewed. In this paper:

- A finite context approach through
**stacked convolutions**is proposed, which can be more efficient since they**allow parallelization**over sequential tokens. - A novel simplified gating mechanism,
**Gated Linear Unit (GLU)**, is proposed.

This is a paper in **2016 arXiv **with over **1300 citations**. (Sik-Ho Tsang @ Medium)

# Outline

**Gated Convolutional Networks (GCNN): Network Architecture****Experimental Results**

**1. Gated Convolutional Networks (GCNN): Network Architecture**

- The above architecture will be mentioned part by part from input to output as below.

## 1.1. Motivations of Using CNN over RNN

**Recurrent neural network (RNN)**always needs to wait for previous state, which is**difficult for parallelization**.- The proposed approach use
**CNN**, which convolves the inputs with a function*f*to obtain*H*=*f***w*and therefore has**no temporal dependencies**, so it is**easier to parallelize**over the individual words of a sentence.

## 1.2. Word Embedding as Input

**Words are represented by a vector embedding**stored in a lookup table*D*^(|*V*|×*e*) where |*V*| is the number of words in the vocabulary and*e*is the embedding size. The**input**to the model is**a sequence of words**which are represented by*w*0, ..,*wN***word embeddings**.*E*=[*Dw*0, …,*DwN*]

## 1.3. Gated Linear Unit (GLU)

**The hidden layers**are computed as:*h*0, …,*hL*

- where
is the*σ***sigmoid**function and**⨂**is the**element-wise product**between matrices. - When convolving inputs, care is needed that
*hi*does not contain information from future words. Zero-padding is used to pad the input to handle this problem.

The output of each layer is a linear projection

X*W+bmodulated by the gates (X*V+c), which is calledGated Linear Units (GLU).

## 1.4. Stacking GLU

**Stacking multiple layers**on top of the input*E*gives a representation of the context for each word.*H*=*hL*○*…*○*h*0(*E*)**The convolution and the gated linear unit in a pre-activation residual block**(Pre-Activation ResNet).- The blocks have a bottleneck structure for computational efficiency and each block has up to 5 layers.
- (Please feel free to read Pre-Activation ResNet if interested.)

## 1.5. Softmax

- The simplest choice to obtain model predictions is to use a
**softmax**layer, but it is**computationally inefficient for large vocabularies**. **Adaptive softmax**which assigns higher capacity to very frequent words and lower capacity to rare words (Grave et al., 2016a), is used.

## 1.6. GCNN Variants

**Gradient clipping**is used where large gradient is clipped.**Weight normalization**is used where weights are normalized in some layers.- Both techniques are used to
**speed up the convergence**.

# 2. Experimental Results

## 2.1. **Google Billion Word **Dataset

**GCNN outperforms the comparable LSTM**results on Google billion words.

GCNN reaches 38.1 test perplexity while the comparable LSTM has 39.8 perplexity.

## 2.2. WikiText-103 Dataset

- An input sequence is an entire Wikipedia article instead of an individual sentence — increasing the average length to 4000 words.

GCNN outperforms LSTMs on this problem as well.

## 2.3. Other Studies

The adaptive softmax approximation greatly reduces the number of operations required to reach a given perplexity.

Models with gated linear units (GLU) converge faster and to a lower perplexity.

- Throughput can be maximized by processing many sentences in parallel to amortize sequential operations.
- In contrast, responsiveness is the speed of processing the input sequentially, one token at a time.

The GCNN with bottlenecks

improves the responsiveness by 20 timeswhilemaintaining high throughput.

- Models with bigger context achieve better results but the results start diminishing quickly after a context of 20.

GLUs perform best.

Weight normalizationandgradient clippingsignificantly speed up convergence.

## Reference

[2016 arXiv] [GCNN/GLU]

Language Modeling with Gated Convolutional Networks

## Natural Language Processing (NLP)

**Sequence Model: 2014** [GRU] [Doc2Vec]**Language Model: 2007 **[Bengio TNN’07] **2013 **[Word2Vec] [NCE] [Negative Sampling] **2016 **[GCNN/GLU]**Sentence Embedding: 2015 **[Skip-Thought]**Machine Translation: 2014** [Seq2Seq] [RNN Encoder-Decoder] **2015** [Attention Decoder/RNNSearch] **2016** [GNMT] [ByteNet] [Deep-ED & Deep-Att]**Image Captioning:** **2015 **[m-RNN] [R-CNN+BRNN] [Show and Tell/NIC] [Show, Attend and Tell]