=Paper=
{{Paper
|id=Vol-2268/paper11
|storemode=property
|title=Text Augmentation for Neural Networks
|pdfUrl=https://ceur-ws.org/Vol-2268/paper11.pdf
|volume=Vol-2268
|authors=Anna Mosolova,Vadim Fomin,Ivan Bondarenko
|dblpUrl=https://dblp.org/rec/conf/aist/MosolovaFB18
}}
==Text Augmentation for Neural Networks==
https://ceur-ws.org/Vol-2268/paper11.pdf
Text Augmentation for Neural Networks
Anna V. Mosolova1 , Vadim V. Fomin2 , and Ivan Yu. Bondarenko1
Novosibirsk State University1 ,
a.mosolova@g.nsu.ru
i.yu.bondarenko@gmail.com
National Research University Higher School of Economics2
wadimiusz@gmail.com
Abstract. This study considers the problem of using small text datasets
for learning of neural networks. We explore the method used for image
and sound datasets that augments data in order to increase the perfor-
mance of models trained on it. We propose a method for augmenting
that is based on synonymy.
Keywords: NLP, neural networks, small datasets, synonymy
1 Introduction
Natural language processing is an actively developing area today. Machine learn-
ing develops in this direction, and developers need for their approaches a lot of
labeled data, but it costs a lot of hours of human’s work. So, there is a need
for increasing amount of data which was labeled earlier. These methods already
exist in other parts of machine learning such as image classification, speech, and
sound recognition, but all technologies that can be used for images and sounds
are not suitable for text because of the danger of losing the sense of a sentence.
These methods are named data augmentation and they are a common way to
increase the performance of the model, avoid overfitting and improve the model’s
robustness. In this paper, we suggest a method for text augmentation that can
improve the performance, does not very computationally cost and allows not
losing the sense of the sentence. The paper consists of Related Work where we
present some augmentation technologies, Methods where the model is described,
Dataset provides the information about data for experiments and Experiments
and Results represent the results of our work. In Conclusion part, some future
goals are outlined.
2 Related Work
Data augmentation is a common problem in many areas of machine learning
because it enables models to generalize better. This is crucial for fields where a
good generalization is a challenging task, e. g. those where one must rely on small
datasets. The problem of data augmentation has a certain history of research
in certain tasks, such as picture recognition or speech recognition. For instance,
[1] suggests methods of image augmentation based on cropping, rotating, and
flipping input images. They also suggest using GANs to generate images of
different styles, and a new method that allows a neural net to choose which
additions will improve the classifier. An approach to sound augmentation is
suggested in [2], which proposed a method that consists in changing the speed of
an audio signal, producing 3 versions of the original with speed factors of 0.9, 1.0
and 1.1. A Python library for sound data augmentation has been suggested in [3].
All of these methods allow increasing the performance of a model without using
any additional data. However, there are no such methods for text augmentation,
so our proposal will be one of the first open-source solutions.
3 Methods
3.1 Algorithm
We propose an approach to text augmentation which consists in using synony-
mous words instead of original ones without losing of sentence’s sense. The pro-
cess of augmentation runs in several steps which we will describe below. The first
step implies excluding all words that do not require replacing like pronouns, con-
junctions, prepositions, and articles so that they remain intact. The nesessity to
leave such words intact is explained by the fact that such words tend to have no
synonyms and by its function which is to define the relationships of other words
rather then to introduce a meaning. The second step is to find the synonyms for
some words in the sentence. The words to be substituted are picked randomly
depending on the initial settings (specifically, the percentage of replaced words).
For example, if a sentence consists of 10 words and augmentation was set for
25%, the algorithm will substitute 2 words in the sentence. The choice of the syn-
onyms is also random. During the next step, new words are put in the places of
changed words and the algorithm returns the resulting sentence. The algorithm
has a parameter that allows increasing the number of sentences in the corpora
n-tuply. For example, Figure 1 shows a 7-times augmentation. Also, there is one
additional option that saves writing in capital letters (This option is presented
in Figure 2).
Fig. 1. An example of a 7-times augmentation with 25% changes
Fig. 2. The alrogithm of augmentation
3.2 Synonymy
The procedure of replacing the words with synonyms described in the paragraph
2.1 was realized by means of WordNet [4]. WordNet contains sets of synonymous
words and represents a base of words which are related in some other ways. We
used WordNet as one of the modules in NLTK [5]. Also, we used POS-tagging
from NLTK for disambiguation of part-of-speech in the sentence. It caused some
problems because POS-tags in NLTK differ from POS-tags in WordNet, so we
added a module for changing it to the desired form.
3.3 Realization
We used for the realization of augmentation Python 3.6 and NLTK library,
because it provides access to WordNet’s base.
4 Dataset
It is the Toxic Comment Classification Challenge, a competition launched by
Kaggle, that inspired us to test text data augmentation as an approach. The aim
of the competition was to classify comments written by Wikipedia users against
6 binary classifications, each binary classification representing a certain type of
toxicity. Thus, we used the dataset from this competition for our experiments
and evaluation. The dataset is available at https://www.kaggle.com/c/jigsaw-
toxic-comment-classification- challenge/data. The train set consisted of 159 571
samples, each of which was assigned 6 class labels, according to the 6 classifica-
tion tasks. The test set consisted of 153 164 samples. The 6 binary classifications
are related to the classes of toxic, severe toxic, obscene comments, threats, insults
and identity hate. Each class is illustrated by examples 1–6 correspondingly:
(1) Bye! Don’t look, come or think of comming back! Tosser.
(2) SHUT UP, YOU FAT POOP, OR I WILL KICK YOUR ASS!!!
(3) A pair of jew-hating weiner nazi schmucks.
(4) Hi! I am back again! Last warning! Stop undoing my edits or die!
(5) Hey, you freaking hermaphrodite. Please unprotect your user page; I would
like to move it to a more suitable title or three.
(6) Bla bla bla....suck it Irishguy =)
The number of samples and their percentage for each class in the dataset is
presented in Table 1.
Type Samples Percentage
Toxic 15249 9,6%
Severe toxic 1959 1,2%
Obscene 8449 5,3%
Threat 478 0,3%
Insults 7877 4,9%
Identity hate 1405 0,9%
Overall 159571 100%
Table 1. Dataset structure
Every comment that is marked as severe toxic is also labeled toxic. This is not
the case with other classes. It is obvious in the table above that the classes are
not quite balanced, e. g. the number of samples in the class ’toxic’ is drastically
larger than the number of comments that contain threats. It is also evident in the
examples above that, while some classes are largely dependent on the presence
of specific words in comments, others depend on the meaning of the sentence on
the whole. For instance, classes ‘identity hate’ and ‘obscene’ rely on the presence
of words that signify words that either insult a nation, a political view etc. or
are obscene.
5 Experiments and results
5.1 Model
To solve the problem suggested in the competition, we used a convolutional neu-
ral network with 128 feature maps, 6 convolutional layers, 6 pooling layers and 2
dense layers and a dropout of 0.5. The feature representation of a sentence that
was used as an input for the neural network consisted of vector representations
of each word in a sentence. As a source of vector representations, we tried two
embeddings trained upon the training set described in section 4: a word2vec
model as a word embedding and a word2vec trained to predict character ngrams
as a character embedding. The result is presented in Table 2.
5.2 Metrics
As a metric it was used ROC-AUC in this competition. The score of an algorithm
is the average of the individual AUCs of each predicted type of toxicity.
Model Public score Private Score
CNN with character embeddings 0.9065 0.8933
CNN with character embeddings and
with a 6 times augmentation for 25% 0.9436 0.9446
of all words
CNN with word embeddings 0.9752 0.9742
CNN with word embeddings
and with a 6 times augmentation for 25% 0.9743 0.9721
of all words
Table 2. Results of the Kaggle competition
6 Analysis
It is obvious from the evaluation presented above that text data augmentation
appeared capable of making character embeddings more relevant for classifica-
tion but did not affect the usefulness word embeddings in any way. This is be-
cause vector representations of synonymous words in word embeddings are very
close. As a result, the artificial samples from the augmented training set are very
close to the existing ones, which is not the case for character embeddings.
7 Conclusion and Future Work
Data augmentation has been shown to produce promising ways to increase the
accuracy of classification tasks. In this paper, we proposed an algorithm that
worked well in the competition from Kaggle and it can be used by researchers as
it free distributed on gitlab. We are going to develop our augmentation model
and add the possibility of augmenting Russian texts using synonyms from Wik-
tionary.
References
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