Transcript Generating Text with Recurrent Neural Networks

GENERATING TEXT WITH RECURRENT
NEURAL NETWORKS
Ilya Sutskever, James Martens, and Geoffrey Hinton, ICML 2011
2013-4-1
Institute of Electronics, NCTU
指導教授: 王聖智 S. J. Wang
學生
: 陳冠廷 K. T. Chen
2
Outline
• Introduction
• Motivation
• What is the RNN?
• Why do we choose RNN to solve this problem?
• How to train RNN?
• Contribution
• Character-Level language modeling
• The multiplicative RNN
• The Experiments
• Discussion
3
Outline
• Introduction
• Motivation
• What is the RNN?
• Why do we choose RNN to solve this problem?
• How to train RNN?
• Contribution
• Character-Level language modeling
• The multiplicative RNN
• The Experiments
• Discussion
4
Movation
• Read some sentences and then try to predict next character.
Easter is a Christian festival
and holiday celebrating the
resurrection of Jesus Christ..
?
Easter is a Christian festival
and holiday celebrating the
resurrection of Jesus Christ
on the third day after his
crucifixion at Calvary as
described in the New
Testament.
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Recurrent neural networks
• A recurrent neural network (RNN) is a class of neural network
where connections between units form a directed cycle
Feed-forward neural network
output
Recurrent neural network
output
hidden
hidden
input
input
6
Why do we choose RNNs?
• RNNs are suitable to deal with sequential data.(memory)
• RNNs are neural network in time
predictions
𝑊ℎ𝑜
𝑊ℎ𝑜
𝑊ℎ𝑜
𝑊ℎℎ
𝑊ℎℎ
𝑊𝑣ℎ
𝑊𝑣ℎ
hiddens
𝑊𝑣ℎ
inputs
t -1
t
t +1
time
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How to train RNN?
• Backpropagation through time (BPTT)
• The gradient is easy to compute with backpropagation.
• RNNs learn by minimizing the training error.
predictions
𝑊ℎ𝑜
𝑊ℎ𝑜
𝑊ℎ𝑜
𝑊ℎℎ
𝑊ℎℎ
𝑊𝑣ℎ
𝑊𝑣ℎ
hiddens
𝑊𝑣ℎ
inputs
t -1
t
t +1
time
9
RNNs are hard to train
• They can be volatile and can exhibit long-range sensitivity
to small parameter perturbations.
• The Butterfly Effect
• The “vanishing gradient problem” makes gradient descent
ineffective.
outputs
hiddens
inputs
time
10
How to overcome vanishing gradient?
• Long-short term memory.(LSTM)
• Modify the architecture of neural network.
data write keep read
• Hessian-Free optimizer. (James Martens et al. 2011.)
• Base on the Newton’s method + conjugate gradient algorithm
• Echo State network.
• Only learn the hidden-output weighted.
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Outline
• Introduction
• Motivation
• What is the RNN?
• Why do we choose RNN to solve this problem?
• How to train RNN?
• Contribution
• Character-Level language modeling
• The multiplicative RNN
• The Experiments
• Discussion
12
Character-Level language modeling
• The RNN observes a sequence of characters.
• The target output at each time step is defined as the input
character at the next time-step.
o
“Hell”
“Hello”
target
“Hel”
l
“He”
l
“H”
e
H
e
l
l
o
Hidden state
stores relevant
Information.
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The standard RNN
𝑊ℎℎ
ℎ𝑡 = tanh(𝑊ℎ𝑥 𝑥𝑡 + 𝑊ℎℎ ℎ𝑡−1 + 𝑏ℎ )
…….
…….
𝑜𝑡 = 𝑊𝑜ℎ ℎ𝑡 + 𝑏𝑜
𝑊ℎ𝑥
H
……
character: 1-of-86
Softmax
Predict distribution
for next character.
• The current input 𝑥𝑡 is transformed via the visible-to-
hidden weight matrix 𝑊ℎ𝑥 ,and then contributes additively
to the input for the current hidden state.
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Some motivation from model a tree
i
..fixi
..fix
e
..fixe
n
.fixin
• Each node is a hidden state vector. The next character
must transform this to a new node.
• The next hidden state needs to depend on the conjunction
of the current character and the current hidden
representation.
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The Multiplicative RNN
• They tried several neural network architectures and found
the “Multiplicative-RNN” (MRNN) to be more effective than
the regular RNN
The weight matrix is
chosen by the current
character
Current input
character
ℎ𝑡 = tanh(𝑊ℎ𝑥 𝑥𝑡 + 𝑊ℎℎ
𝑜𝑡 = 𝑊𝑜ℎ ℎ𝑡 + 𝑏𝑜
𝑥𝑡
ℎ𝑡−1 + 𝑏ℎ )
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The Multiplicative RNN
• Naïve implementation : assign a matrix to each character
• This requires a lot of parameters. (86*1500*1500)
• This could make the net overfit.
• Difficult to parallelize on a GPU
• Factorize the matrices of each character
• Fewer parameters
• Easier to parallelize
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The Multiplicative RNN
• We can get groups a and b to interact multiplicatively by
using “factors”
𝒗𝑓
f
Group c
Group a
𝒖𝑓
𝒄𝑓 = (𝒃𝑇 𝒘𝑓 )(𝒂𝑇 𝒖𝑓 )𝒗𝑓
𝒄𝑓 = (𝒃𝑇 𝒘𝑓 )(𝒗𝑓 𝒖𝑓 𝑇 ) 𝒂
Scalar
coefficient
𝒘𝑓
Group b
Outer product
transition matrix
with rank 1
𝒃𝑇 𝒘𝑓 𝒗𝑓 𝒖𝑓 𝑇
𝒄=
𝑓
𝒂
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The Multiplicative RNN
𝒃𝑇 𝒘𝑓 𝒗𝑓 𝒖𝑓 𝑇
𝒄=
…….
𝒗𝑓
𝒘ℎ𝑓
H
……
character: 1-of-86
…….
f
…….
1500
hidden
units
𝒖𝑓
𝑓
1500
hidden
units
Predict distribution
for next character.
Each factor f defines a rank
one matrix,𝒗𝑓 𝒖𝑓 𝑇
𝒂
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The Multiplicative RNN
Output
𝑊ℎ𝑜
𝑊ℎ𝑓
𝑊ℎ𝑜
𝑊𝑓ℎ
𝑊𝑣ℎ
𝑊𝑓ℎ
𝑊𝑣ℎ
t
𝑊ℎ𝑓
𝑊ℎ𝑜
𝑊𝑓ℎ
𝑊𝑣ℎ
𝑊𝑣𝑓
𝑊𝑣𝑓
t-1
𝑊ℎ𝑓
𝑊ℎ𝑜
t+1
𝑊ℎ𝑓
𝑊𝑓ℎ
𝑊𝑣ℎ
𝑊𝑣𝑓
t +2
Input
characters
Time
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The Multiplicative RNN:Key advantages
• The MRNN combines conjunction of contexts and
characters more easily:
Predict “i,e,_”
Predict “n”
fix
fixi
i
• The MRNN has two nonlinearities per timestep,whick
make its dynamics even ricker and more powerful.
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Outline
• Introduction
• Motivation
• What is the RNN?
• Why do we choose RNN to solve this problem?
• How to train RNN?
• Contribution
• Character-Level language modeling
• The multiplicative RNN
• The Experiments
• Discussion
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The Experiments
• Training on three large datasets
• ~1GB of the English Wikipedia
• ~1GB of articles from New York Times
• ~100MB of JMLR and AISTATS paper
• Compare with the Sequence Memorizer (Wood et al.) and
PAQ (Mahoney et al.)
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Training on subsequences
millions long
This is an extremely long string of text………………………………………………………………………….
250
This is an extre ………
his is an extrem……..
is is an extreme……...
The subsequences
s is an extremel……...
is an extremely……...
is an extremely .......
s an extremely l.......
…..
Compute the gradient and
the curvature on subset of
the subsequences.
Use a different subset at
each iteration
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Parallelization
• Use HF optimizer to evaluate the gradient and curvature
on large minibatches of data.
Data
GPU
GPU
+
gradient
+
curvature
GPU
GPU
GPU
GPU
GPU
GPU
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The architecture of model
• Use 1500 hidden units and 1500 multiplicative factors on 250-
long sequences.
......
1500
predicetions
......
….
….
….
….
….
….
….
….
......
hiddens
input
500
• Arguably the largest and deepest neural network ever trained.
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Demo
• The MRNN extracts “higher level information”, stores it for
many timesteps ,and uses it to make a prediction.
• Parentheses sensitivity
• (Wlching et al. 2005) the latter has received numerical testimony without much
•
•
•
•
•
•
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Outline
• Introduction
• Motivation
• What is the RNN?
• Why do we choose RNN to solve this problem?
• How to train RNN?
• Contribution
• Character-Level language modeling
• The multiplicative RNN
• The Experiments
• Discussion
28
Discussion
• The MRNN model generated text contains very few non-
words. (e.g., “cryptoliation”, “homosomalist”). This let
MRNN can deal with real words that it didn’t see in the
training set.
• If they have more computational power, they could train
much bigger MRNNs with millions of units and billions of
connections.
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Reference
• Generating Text with Recurrent Neural Networks, Ilya Sutskever,
James Martens, and Geoffrey Hinton, ICML 2011
• Factored Conditional Restricted Boltzmann Machines for
Modeling Motion Style, GrahamW. Taylor, Geoffrey E. Hinton
• Coursera : Neural Networks for Machine Learning ,Geoffrey
Hinton
• http://www.cs.toronto.edu/~ilya/rnn.html