The document provides an overview of practical deep learning techniques for natural language processing, focusing on text classification and sentiment analysis using convolutional networks and ResNet models. It includes key points on model architecture, performance metrics, data handling strategies, and suggestions for hyperparameter optimization. Additionally, it emphasizes practical tips for training deep learning models effectively.

1. Practical Deep Learning for NLP
Maarten Versteegh
NLP Research Engineer

2. Overview
● Deep Learning Recap
● Text classification:
– Convnet with word embeddings
● Sentiment analysis:
– ResNet
● Tips and tricks

3. What is this deep learning thing again?

4. Input
Hidden
Output
Activation
Error

5. Rectified Linear Units
Backpropagation involves repeated multiplication with derivative of activation function
→ Problem if result is always smaller than 1!

6. Text Classification

7. Traditional approach: BOW + TFIDF
“The car might also need a front end alignment”
"alignment" (0.323)
"also" (0.137)
"car" (0.110)
"end" (0.182)
"front" (0.167)
"might" (0.178)
"need" (0.157)
"the" (0.053)
"also need" (0.343)
"car might" (0.358)
"end alignment" (0.358)
"front end" (0.296)
"might also" (0.358)
"need front" (0.358)
"the car" (0.161)

8. F1-Score*
BOW+TFIDF+SVM Some number
20 newsgroups performance
(*) Scores removed

9. Deep Learning 1: Replace Classifier
Hidden x 256
x 512
x 1000
BOW
Features
Hidden
Output

10. from keras.layers import Input, Dense
from keras.models import Model
input_layer = Input(shape=(1000,))
fc_1 = Dense(512, activation='relu')(input_layer)
fc_2 = Dense(256, activation='relu')(fc_1)
output_layer = Dense(10, activation='softmax')(fc_2)
model = Model(input=input_layer, output=output_layer)
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(bow, newsgroups.target)
predictions = model.predict(features).argmax(axis=1)

11. F1-Score*
BOW+TFIDF+SVM Some number
BOW+TFIDF+SVD+ 2-layer NN Some slightly higher number
20 newsgroups performance
(*) Scores removed

12. What about the deep learning promise?

13. Convolutional Networks
Source: Andrej Karpathy

14. Pooling layer
Source: Andrej Karpathy

15. Convolutional networks
Source: Y. Kim (2014) Convolutional Networks for Sentence Classification

16. Word embedding

17. from keras.layers import Embedding
# embedding_matrix: ndarray(vocab_size, embedding_dim)
input_layer = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')
layer = Embedding(
embedding_matrix.shape[0],
embedding_matrix.shape[1],
weights=[embedding_matrix],
input_length=max_sequence_length,
trainable=False
)(input_layer)

18. from keras.layer import Convolution1D, MaxPooling1D,
BatchNormalization, Activation
layer = Embedding(...)(input_layer)
layer = Convolution1D(
128, # number of filters
5, # filter size
activation='relu',
)(layer)
layer = MaxPooling1D(5)(layer)

19. Performance
F1-Score*
BOW+TFIDF+SVM Some number
CBOW+TFIDF+SVD+NN Some slightly higher number
ConvNet (3 layers) Quite a bit higher now
ConvNet (6 layers) Look mom, even higher!
(*) Scores removed

20. Sentiment Analysis

21. Data Set
Facebook posts from media organizations:
– CNN, MSNBC, NYTimes, The Guardian, Buzzfeed,
Breitbart, Politico, The Wall Street Journal, Washington
Post, Baltimore Sun
Measure sentiment as “reactions”

22. Title Org Like Love Wow Haha Sad Angry
Poll: Clinton up big on Trump in Virginia CNN 4176 601 17 211 11 83
It's a fact: Trump has tiny hands. Will
this be the one that sinks him?
Guardian 595 17 17 225 2 8
Donald Trump Explains His Obama-
Founded-ISIS Claim as ‘Sarcasm’
NYTimes 2059 32 284 1214 80 2167
Can hipsters stomach the unpalatable
truth about avocado toast?
Guardian 3655 0 396 44 773 69
Tim Kaine skewers Donald Trump's
military policy
MSNBC 1094 111 6 12 2 26
Top 5 Most Antisemitic Things Hillary
Clinton Has Done
Breitbart 1067 7 134 35 22 372
17 Hilarious Tweets About Donald
Trump Explaining Movies
Buzzfeed 11390 375 16 4121 4 5

23. Go deeper: ResNet
Convolutional Layers with shortcuts
He et al. Deep Residual Learning
for Image Recognition

24. Go deeper: ResNet
input_layer = ...
layer = Convolution1D(128, 5, activation='linear')
(input_layer)
layer = BatchNormalization()(layer)
layer = Activation('relu')(layer)
layer = Convolution1D(128, 5, activation='linear')(layer)
layer = BatchNormalization()(layer)
layer = Activation('relu')(layer)
block_output = merge([layer, input_layer], mode='sum')
block_output = Activation('relu')(block_output)

25. It's a fact: Trump has tiny hands.
(EMBEDDING_DIM=300)
ResNet Block
…
ResNet Block
The Guardian
(1-of-K)
Conv (128) x 10
%
Title + Message
News Org
MaxPooling
Dense
Dense

26. Cherry-picked predicted response
distribution*
Sentence Org Love Haha Wow Sad Angry
Trump wins the election Guardian 3% 9% 7% 32% 49%
Trump wins the election Breitbart 58% 30% 8% 1% 3%
*Your mileage may vary. By a lot. I
mean it.

27. Tips and Tricks

28. Initialization
● Break symmetry:
– Never ever initialize all your weights to
the same value
● Let initialization depend on activation
function:
– ReLU/PReLU → He Normal
– sigmoid/tanh → Glorot Normal

29. Choose an adaptive optimizer
Source: Alec Radford
Choose an adaptive optimizer

30. Choose the right model size
● Start small and keep adding layers
– Check if test error keeps going down
● Cross-validate over the number of units
● You want to be able to overfit
Y. Bengio (2012) Practical
recommendations for gradient-based
training of deep architectures

31. Don't be scared of overfitting
● If your model can't overfit, it also can't learn enough
● So, check that your model can overfit:
– If not, make it bigger
– If so, get more date and/or regularize
Source: wikipedia

32. Regularization
● Norm penalties on hidden layer weights, never
on first and last
● Dropout
● Early stopping

33. Size of data set
● Just get more data already
● Augment data:
– Textual replacements
– Word vector perturbation
– Noise Contrastive Estimation
● Semi-supervised learning:
– Adapt word embeddings to your domain

34. Monitor your model
Training loss:
– Does the model converge?
– Is the learning rate too low or too high?

35. Training loss and learning rate
Source: Andrej Karpathy

36. Monitor your model
Training and validation accuracy
– Is there a large gap?
– Does the training accuracy increase
while the validation accuracy
decreases?

37. Training and validation accuracy
Source: Andrej Karpathy

38. Monitor your model
● Ratio of weights to updates
● Distribution of activations and gradients
(per layer)

39. Hyperparameter optimization
After network architecture, continue with:
– Regularization strength
– Initial learning rate
– Optimization strategy (and LR decay
schedule)

40. Friends don't let friends do a full grid search!

41. Hyperparameter optimization
Friends don't let friends do a full grid search!
– Use a smart strategy like Bayesian
optimization or Particle Swarm Optimization
(Spearmint, SMAC, Hyperopt, Optunity)
– Even random search often beats grid search

42. Keep up to date: arxiv-sanity.com

43. We are hiring!
DevOps & Front-end
NLP engineers
Full-stack Python engineers
www.textkernel.com/jobs

44. Questions?
Source: http://visualqa.org/