Using Artificial Neural Networks in Dialect Identification in
Less-resourced Languages
The Case of Kurdish Dialects Identification
Hossein Hassani and Oussama H. Hamid
Department of Computer Science and Engineering, University of Kurdistan Hewl
er, Erbil, Kurdistan Region, Iraq
Dialect Classification, Natural Language Processing, Artificial Neural Networks, Machine Learning, Kurdish
Dialect identification/classification is an important step in many language processing activities particularly
with regard to multi-dialect languages. Kurdish is a multi-dialect language which is spoken by a large pop-
ulation in different countries. Some of the Kurdish dialects, for example, Kurmanji and Sorani, have sig-
nificant grammatical differences and are also mutually unintelligible. In addition, Kurdish is considered a
less-resourced language. The classification techniques based on machine learning approaches usually require
a considerable amount of data. In this research, we are interested in using approaches based on Artificial Neu-
ral Network (ANN) in order to be able to identify the dialects of Kurdish texts without the need to have a large
amount of data. We will also compare the outcomes of this approach with the previous work on Kurdish di-
alect identification to compare the performance of these methods. The results showed that the two approaches
do not show a significant difference in their accuracy and performance with regard to long documents. How-
ever, they showed that the ANN approach performs better than traditional approach for the single sentence
classification. The accuracy rate of the ANN sentence classifier was 99% for Kurmanji and 96% for Sorani.
Kurdish is an Indo-European multi-dialect lan-
guage (Hassanpour, 1992). It is mainly spoken in ar-
eas touching Iran, Iraq, Turkey, and Syria and also
by Kurdish communities in other countries such as
Lebanon, Georgia, Armenia, Afghanistan and Kur-
dish diaspora in Europe and North America (Hassani
and Medjedovic, 2016; Foundation Institute Kurde de
Paris, 2017a). The population that speaks the lan-
guage is estimated to be between 36 and 47 million
The most common categorization of Kurdish di-
alects includes Northern Kurdish (Kurmanji), Central
Kurdish (Sorani), Southern Kurdish, Gorani and Za-
zaki (Haig and
Opengin, 2014; Hassani and Med-
jedovic, 2016; Malmasi, 2016). Kurdish is writ-
ten using four different scripts, which are modified
Persian/Arabic, Latin, Yekgirt
u(unified), and Cyril-
lic (Hassani and Medjedovic, 2016) The usage of the
The details that appear in (Foundation Institute Kurde
de Paris, 2017b) do not show the population of Kurdish
diaspora in North America. However, they elsewhere, the
same website has estimate this to be about 26,000 in (Foun-
dation Institute Kurde de Paris, 2017a).
scripts and their popularity differ depending on the
dominance of Persian, Arabic, Turkish, and Cyrillic
in the specific regions (Hassani, 2017b).
The dialect diversity of Kurdish implies that au-
tomatic dialect identification is an essential task in
Kurdish Natural Language Processing (NLP) (Has-
sani and Medjedovic, 2016; Hassani, 2017a). Lan-
guage identification is a fundamental task in NLP
though a straightforward one (Zaidan and Callison-
Burch, 2014). Although dialect identification could
be viewed as language identification, the subtle dif-
ferences that distinguishes one dialect from the other
leads to a more complex NLP and computational
task (Zaidan and Callison-Burch, 2014). The task
of Dialect Identification (DID) is a special case of
the more general problem of Language Identifica-
tion (LID) (Ali et al., 2015; Hassani and Medjedovic,
Inspired by the models that depict the way that the
human brain processes the cognition, Artificial Neu-
ral Network (ANN) has been suggested to be used
in solving a wide range of problems such as pat-
tern recognition and classification (Jain et al., 1996;
Krogh, 2008). scholars (Gl
uge et al., 2010; Rizwan
Hassani H. and H. Hamid O.
Using Artificial Neural Networks in Dialect Identification in Less-resourced Languages - The Case of Kurdish Dialects Identification.
DOI: 10.5220/0006578004430448
2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
et al., 2016; Sunija et al., 2016; Soorajkumar et al.,
2017; Sinha et al., 2017). ANN has been also used
in text classification (Ghiassi et al., 2012; Lai et al.,
2015; Belinkov and Glass, 2016).
We are interested in investigating the performance
and accuracy of ANN in the identification of Kurdish
dialects in textual formats. An efficient dialect iden-
tifier is necessary in Kurdish NLP and CL. Although
different approaches have been taken by researchers
to address the problem of text classification, we pre-
fer to use a simple approach before embarking into a
more complex method. For this we use the percep-
tron model, which was introduced in the 1960s. We
also use a traditional classifier based on Support Vec-
tor Machines (SVM) to compare the performance of
this method with the previous one. Importantly, we
also evaluate our models at the sentence level. That
is, we assess the accuracy of the models when they
are applied to sentences rather than long documents.
The rest of this article is organized as follows.
Section 2 discusses the related work. Section 3 pro-
vides the methodology and how the experiments are
conducted. Section 4 summarizes the findings and
gives the conclusion.
The Parallel Convolutional Neural Network was sug-
gested (Johnson and Zhang, 2015) for text categoriza-
tion. It was proposed as an alternative mechanism for
effective use of word order by the usage of direct em-
bedding of small text regions. The approach is differ-
ent from the bag-of-ngram or word-vector Convolu-
tional Neural Network (CNN). Parallel CNN frame-
work allows the learning of several types of embed-
ding which can be combined together. This combina-
tion is able to let the parts to complement each and to
provide a higher accuracy. According to researchers
who suggested this approach, they have been able to
achieve a state-of-the-art performance on sentiment
classification and topic classification (Johnson and
Zhang, 2015).
A Dynamic Artificial Neural Network (DAN2) al-
gorithm was proposed as an alternative approach for
text classification (Ghiassi et al., 2012). Like the
classical neural networks, DAN2 is also composed of
an input layer, several hidden layers and an output
layer (Ghiassi et al., 2012). However, unlike classi-
cal neural networks, there is no preset number for the
hidden layers (Ghiassi et al., 2012). The experiments
with DAN2 showed that it outperforms the classi-
cal approaches of Machine Learning (ML) which are
used for classification such as Key Nearest Neigh-
bors (KNN) and Supervised Vector Machines (SVM)
(Ghiassi et al., 2012).
A Recursive Neural Network (RecursiveNN or
RNN) (Socher et al., 2011; Socher et al., 2013)
was suggested to be used in parsing natural lan-
guage processing. The experiments with its applica-
tion showed that it outperforms the state-of-the-art ap-
proaches in segmentation, annotation and scene clas-
sification (Socher et al., 2011).
A variant of RecursiveNN, Recursive Convolu-
tional Neural Network (RCNN) (Zhu et al., 2015) is
used in different tasks of NLP in order through mod-
eling the relations between a dependency tree and dis-
tributed representations of a sentence or phrase (Zhu
et al., 2015).
Recurrent Convolutional Neural Networks (Re-
currentCNN) (Lai et al., 2015) was introduced for text
classification. It uses a recurrent structure to capture
the contextual information. It then uses a CNN to con-
structs the representation of text. The results of ap-
plying this approach showed that it outperforms CNN
and Recursive Neural Networks (RecursiveNN) (Lai
et al., 2015).
Character-level Convolutional Networks (Con-
vNets) (Zhang et al., 2015) was applied to text classi-
fication tasks, particularly for discriminating between
similar languages and dialects. The experiments with
Arabic dialect identification showed promising re-
sults (Zhang et al., 2015; Belinkov and Glass, 2016).
The depth and diversity of literature in the context
of ANN application with regard to NLP tasks, in ad-
dition to the current and ongoing research on this area
suggests that to explore the performance of the re-
lated algorithms in Kurdish NLP potentially can con-
tribute to ANN applications. However, in this stage
we start with basic forms of ANN to investigate how
they might work with the dialect diversity and re-
source paucity in Kurdish.
We apply two approaches to identify the dialect of a
Kurdish text. The first approach is based on ANN
and the second is based on traditional classifiers. For
the first approach, we use a Perceptron and for the
second we use SVMs. Both methods are explained in
the following sections.
3.1 Perceptron
An ANN is a suggested architecture based on the way
the human brain works, that is, a network of model
neurons in computer which are able to imitate the pro-
cess of natural neurons whereby they can be trained to
solve different kinds of problems (Krogh, 2008). An
ANN includes an input layer, several hidden layers
and an output layer.
In a text classifier based on ANN, the input units
consists of terms/words, the hidden layers are the
computational units and the output layer represents
class of the inputs (Sebastiani, 2002). A weight
is assigned to each term that act is a parameter in
computation (hidden) layers. To classify a text, the
words/terms weights are given to the network and the
sum of the weights is computed, which leads ot the
identification of the category/class of the text (Sebas-
tiani, 2002).
Backpropagation is a classic method for training
ANNs (Sebastiani, 2002). In this method, a training
document (the weight vector) is processed. If the clas-
sifier is not able to classify the document properly, an
error is raised and “backpropagated” through the net-
work. The network changes the computation parame-
ters in order to correct the decision.
Perceptron is the simplest type of NN classi-
fiers (Sebastiani, 2002) and also it is a kind of Linear
Classifier (Gkanogiannis and Kalamboukis, 2009). It
begins with an initial model which is refined gradually
and iteratively during learning process (Gkanogiannis
and Kalamboukis, 2009).
We apply the modified Perceptron learning rule
which was suggested for tag recommendations in so-
cial bookmarking systems (Gkanogiannis and Kalam-
boukis, 2009) and adapt it as a dialect identifier. The
proposed algorithm is a binary linear classifier and it
combines a centroid with a batch Perceptron classifier
and a modified perceptron learning rule that does not
need any parameter estimation. We use this classifier
to detect whether a text is Kurmanji (Ku) or Sorani
(So). In addition, we use a multilayer Perceptron (Pal
and Mitra, 1992; Kessler et al., 1997) to identify the
text dialect.
For the first case the simple Perceptrons are de-
fined as:
t ) =
1 if
+ c > 0
1 otherwise
t ) =
1 if
+ c > 0
1 otherwise
t ) determines whether a text has been written
in Kurmanji or not;
t ) determines whether a text has been written
in Sorani or not;
W is weights vector;
c is a constant that is tuned during the training pro-
The learning process means the gradual updating
of weight vector (
W ).
We use a multilayer Perceptron in which “all in-
put units connected to all units of the hidden layer,
and all units of the hidden layer connected to all out-
put units” (Kessler et al., 1997) to identify the text di-
alect. This allows us to add more dialects into our ex-
periments and also makes the model ready to be used
in sub-dialect identification in the future.
As we use multilayer Perceptron for multiple class
detection purpose, instead of a sigmoid activation
function, we use a softmax activation function for out-
put detection. The softmax activation function is de-
fined as below:
f (d
) =
shows the inputs to the classifier and
shows the detected dialects.
To minimize the errors in the classifier output a
minimize error function is used. This function is de-
fined as below:
Err =
kact desk
act show the actual outputs;
des show the desired outputs;
nd is the number of dialects.
3.2 Traditional Classifier
We select features for the SVM into two sets of bag-
of-words, one set for Kurmanji dialect which we call
it KuTS, and the other for Sorani dialect which we call
SoTS. We select 10,000 words from our Kurdish cor-
pora. We select KuTS and SoTS based on two criteria.
The first criteria is the frequency of the words in the
related corpus. The second criteria is to have no over-
lap in the words, which is shown by Equation 5.
KuT s SoT s = (5)
We apply the first condition to restrict the train-
ing vectors to the most frequent words. The second
condition is applied to investigate whether the com-
mon vocabulary plays a role in the efficiency of the
3.3 Experiment Plan
We use our Kurdish corpora
for the experiments.
Table 1 gives the general information about this cor-
Table 1: The number of tokens and word forms inthe Kur-
dish corpora used in this research.
Tokens Word forms
Kurmanji 1,330,443 98,253
Sorani 384,586 67,056
We use 50% of the corpus for training, 10% for
development, and the remaining 40% as test data. We
have decided to use only 50% of the data for training
because as it was mentioned in Section 1, Kurdish is
considered a less-resourced language and we are in-
terested in investigating the efficiency of using ANN
in the absence of large amount of data.
3.4 Results
Table 2 shows the accuracy of the experiments based
on the Perecptron classifier. The table shows the ac-
curacy of the approach for the long texts and single
sentences in the test dataset separately.
Table 2: The results of testing the Perceptron classifier.
Long texts Sentences
Kurmanji 75% 99%
Sorani 72% 96%
Table 3 shows the accuracy of the experiments
based on the traditional classifier. The table shows
the accuracy of the approach for the long texts and
single sentences in the test dataset separately.
Table 3: The results of testing the traditional classifier.
Long texts Sentences
Kurmanji 74.5% 97%
Sorani 71.55% 93%
Table 4 shows the accuracy of the experiments
based on the multilayer Perceptron. The table shows
the accuracy of the approach for the long texts and
single sentences in the test dataset separately.
The corpora consists of variety of texts in Kurmanji and
Sorani which is not annotated. It is currently not available
for public use.
Table 4: The results of testing the multilayer Perceptron
Long texts Sentences
Kurmanji 88% 58%
Sorani 96% 49%
Table 5 shows the samples of the words and their
frequency which was created during the training pro-
Table 5: The samples of the words and their frequency from
the dataset which was created during the training process.
The classes 0 and 1 denote Kurmanji and Sorani dialects,
Word Class Frequency
axa 0 8
axo 1 5
ı 1 2
bawe 1 1
ı 0 13
ı 1 10
belł 0 179
e 1 8
ber 0 2089
ber 1 54
błdenge 1 1
e 0 69
kar 0 242
kar 1 27
saya 0 33
ı 1 3
eber 1 28
eberekan 1 49
e 0 9
e 1 13
In the next section we discuss the presented re-
3.5 Discussion
The results showed that the accuracy of ANN clas-
sifier did not present a significant difference against
the traditional classifier if the inputs were long texts.
However, it showed a considerable difference with re-
gard to sentence classification. On the other hand for
Sorani dialect the accuracy in both cases is lower than
the rate of classification fo Kurmanji texts/sentence.
The reasons for this should be investigated further.
However, the preliminary studies suggest that this is
the consequence of the smaller dataset that was avail-
able for the training process. However, we need to
conduct more experiments and to add texts in other
Kurdish dialects in order to assess the accuracy of
the model and importantly, the efficiency of the ap-
proaches in the absence of a large amount of data.
The multilayer Perceptron showed a different fig-
ure. While it performed well for the long texts, it per-
formed quite poor for the sentence classification. This
shows that the classifier has not been able to guess the
dialect with a high accuracy for short sentences. The
preliminary investigations show that this primarily is
due to the close relation between Kurmanji and So-
rani dialects which makes it difficult to differentiate
between the two dialects based on short sentences.
However, it requires further study to find out other
possible reasons for this outcome.
The article discussed the importance of the task of di-
alect identification in Kurdish NLP and CL. Through
emphasizing the dialect diversity and resource paucity
we presented the idea of using ANN to identify the
different Kurdish dialects in Kurdish texts. We in-
vestigated the efficiency and accuracy of ANN based
classifiers in the absence of large amount of texts
or corpora. We also compared the outcomes of
this approach with the previous work (see (Hassani
and Medjedovic, 2016)) on automatic Kurdish dialect
identification to compare the accuracy and perfor-
mance among the two approaches. The results sug-
gested that while the two approaches do not show
a significant difference in their accuracy and perfor-
mance with regard to long documents, the ANN ap-
proach performs better than traditional approach for
the single sentence classification. However, because
we were not able to find any baseline for the sentence
classifiers in Kurdish dialect identification studies, we
were not able to compare this part of the outcome.
Nevertheless, the sentence classifier performed with a
high accuracy at 99% for Kurmanji and 96% for So-
The multilayer Perceptron acted differently. It
provided quite a poor result for the sentence classi-
fication, while showed a reasonable accuracy for the
long texts. The early investigations suggest that this
behavior could be justified based on the close rela-
tion between Kurmanji and Sorani dialects. However,
more research is needed to become more certain about
this situation and to enhance the classifier to be able
to classify short sentences with a higher accuracy.
As for future work, we are interested in expanding
the research to cover the texts written in other scripts
for example, Persian/Arabic. We are also interested
in including other Kurdish dialects such as Hawrami
in the classification process. In addition, we believe
that the multilayer Perceptron requires further studies,
particularly on the error minimization process. We
are planning to work on the mentioned areas in an
extended paper that follows the current work.
The authors would like to thank the anonymous re-
viewers for their constructive suggestions and recom-
mendations which have improved the content of the
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