=Paper=
{{Paper
|id=Vol-2266/T2-8
|storemode=property
|title=NLPRL@INLI-2018: Hybrid gated LSTM-CNN model for Indian Native Language Identification
|pdfUrl=https://ceur-ws.org/Vol-2266/T2-8.pdf
|volume=Vol-2266
|authors=Rajesh Kumar Mundotiya,Manish Singh,Anil Kumar Singh
|dblpUrl=https://dblp.org/rec/conf/fire/MundotiyaSS18
}}
==NLPRL@INLI-2018: Hybrid gated LSTM-CNN model for Indian Native Language Identification==
<pdf width="1500px">https://ceur-ws.org/Vol-2266/T2-8.pdf</pdf>
<pre>
 NLPRL@INLI-2018: Hybrid gated LSTM-CNN
 model for Indian native language identification

       Rajesh Kumar Mundotiya1 , Manish Singh2 , and Anil Kumar Singh1
       1
           Department of Computer Science and Engineering, IIT(BHU), Varanasi
                  {rajeshkm.rs.cse16, aksingh.cse}@iitbhu.ac.in
                      2
                        Department of Linguistics, BHU, Varanasi
                            maneeshhsingh100@gmail.com



           Abstract. Native language identification (NLI) focuses on determining
           the native language of the author based on the writing style in En-
           glish. Indian native language identification is a challenging task based
           on users comments and posts on social media. To solve this problem, we
           present a hybrid gated LSTM-CNN model to solve this problem. The
           final vector of a sentence is generated at hybrid gate by joining the two
           distinct vector of a sentence. Gate seeks the optimum mixture of the
           LSTM and CNN level outputs. The input word for LSTM and CNN
           are projected into high-dimensional space by embedding technique. We
           obtained 88.50% accuracy during training on the provided social media
           dataset, and 17.10% is reported in the final testing done by Indian native
           language identification (INLI) workshop organizers.

           Keywords: Bi-LSTM · CNN · Glove.


1     Introduction
Native Language Identification is a process to automatically identify the native
language of an author by the writing or the speaking accent of his or her in
another language that is acquired as a second language [1]. It can identify the
writing structure based on the authors linguistic background. It can be used for
several applications namely authorship profiling and identification, forensic anal-
ysis, second language identification and educational applications. English is one
of the well known and commonly used languages among humans. In this shared
task, the goal is to identify the Indian native language written on social media
as post or comment in English. Indian native language includes Bengali, Hindi,
Kannada, Malayalam, Tamil and Telugu. The assumption behind this dataset
collection is that only native language speakers will read native language news-
papers [1] [12].
We have tackled this problem by supervised learning as classification problem
but the main challenges for this are insufficient dataset size. There are couple of
datasets used in past research which are freely available. International Corpus of
Learner English (ICLE)3 corpus is one of the first appearing in the early studies.
3
    https://uclouvain.be/en/research-institutes/ilc/cecl/corpora.html
2      Rajesh Kumar Mundotiya et al.

It was publicly used for prediction of native language of learner based on his/her
writing style. It was released in 2002 and updated in 2009.

   In the following sections; we mentions related work in Section 2, we describe
the proposed model and training procedure in Section 3, we show the result and
analysis in Section 4 and finally, draw conclusion in Section 6.


2   Related Work
Native language identification is a new and significant problem. Language learn-
ers are prone to make mistakes similarly if machines can learn the same tenden-
cies of making mistakes then it may help in developing systems for educational
domain. Several researchers worked on this problem and similar problem like sec-
ond language acquisition. One of the earliest work, Tomokiyo and Jones (2001)
tried to discriminate non-native statements from native statements, written in
English by Nave Bayes [2].
Kochmar et al. (2011) has performed experiments on prediction of the native
languages of Indo-European learners. He treated this problem as binary classifi-
cation and use linear kernel SVM. The features used for prediction were n-grams
and words. Also, The errors were tagged manually within the corpus [3]. Besides
this, some other [4], [5] also used the SVM with different features.
In the recent past, word embedding and document embedding has gained much
attention along with other features. Vector representations for documents were
generated with distributed bag-of-words architectures using Doc2Vec tool. The
authors developed a native language classifier using document and word embed-
ding with an accuracy of 82% for essays and 42% on speech data [6]. Yang et
al. (2016) have purposed hierarchical attention network for classification prob-
lem. They required vast corpus size attend the significant word and sentence by
attention mechanisms [10]. Kim (2014) used the convolutional neural network
and got a state-of-the-art accuracy, but it can hold contextual information till
window-size. [11]
In 2017 another shared task on NLI was organized. The corpus included essays
and transcripts of utterances. According to Malmasi et. al. (2017), the ensemble
methods and meta-classifiers with syntactic or lexical features were the most
effective systems [7].


3   Model Description
The model architecture is showed in figure 1.

   Each document includes m sentences and each sentence within a document
consists of n words. The word level input wi∗n projected into high-dimensional
vector space with the help of pretrained glove English word embedding, wi∗n ∈<k ,
where k is word vector’s dimension, i and n represents sentence and word re-
spectively. The word level input is converted into sentence level input by using a
                                  Hybrid gated LSTM-CNN model for INLI            3




               Fig. 1. Hybrid gated LSTM-CNN system architecture.


bidirectional LSTM. This is achieved by linearly combining the last hidden state
of forward and backward LSTM.

                          xword
                           st   = Wsf hst   r st
                                       f + Ws hr + bs                           (1)

Where, st ∈ S, S is the total sentences in a document and hfst , hrst ∈ <k are
the forward and backward LSTM’s end hidden states, respectively. wsf , wsr ∈
<k∗k and bs ∈ <k are trainable parameters and xword   st    ∈ <k is the final vector
representation of the sentence st . Similarly, with the help of bidirectional LSTM,
we will retrieve the single vector representation of the document. It will take
vector representation of the sentences [10] as input.

                          xword
                           dt   = Wdf hdt    r dt
                                       f + W d h r + bd                         (2)

Where dt ∈D, D is the number of documents which is composed of the combina-
tion of the last hidden state of the forward and backward LSTM, represented by
hfdt , hrdt ∈ <k respectively. wdf , wdr ∈ <k∗k and bd ∈ <k are trainable parameters
and xworddt   ∈ <k is the final vector representation of the sentence dt .
Convolutional neural network [11] helps to extract features by applying convolv-
ing filters on input word vector. Let wt ∈<k , where k th is the dimensional vector
like to the ith word in the document. A document length of length n ∗ m is
represented as:

                   w1:n∗m = w1 ⊕ w2 ⊕ w3 ⊕ w4 ⊕, ...., ⊕wn∗m                    (3)
4       Rajesh Kumar Mundotiya et al.

A convolution operation involves a filter and the size of this filter size shows
h-gram of words. The filter moves over the entire document {w1:h , w2:h+1 , ....
, wn∗m−h+1:n∗m } and generate features {c1 , c2 , ...., cn∗m−h+1 }. These generated
features produce a feature map c.

                             c = [c1 , c2 , ...., cn∗m−h+1 ]                    (4)

Now we want to extract most important feature from feature map, so we apply
                                                                        ∧
max-over-pooling on this feature map that gives maximum value c= max{c}
as the feature crossponding to this particular filter. This process extracts one
feature from one filter. This model uses multiple filters with different h-gram to
extract multiple features. These different extracted features concatenated and
flatten before passing in the dense layer. We have D documents, for each docu-
ment the entire process is repeated. The final vector representation of the sen-
tence dt is xchar    k
              dt ∈< where dt ∈D.
The assumption is that, bidirectional LSTM can capture the entire document
features regarding language model into xword dt   and n-gram features for convolu-
tional neural network into xchar
                             dt  . If both  combine, then it may help to distinguish
the Indian languages. The gated word-char layer combines these two types of vec-
tor generation from bidirectional LSTM and convolutional neural network. The
process of mixing generated vectors xworddt    and xchar
                                                    dt   is done by gate gdt , where
gdt is also a dense layer with sigmoid activation function.

                              gdt = σ(vgT xword
                                           dt   + bg )                          (5)

                         xdt = (1 − gdt )xword
                                          dt   + gdt xchar
                                                      dt                        (6)
Where vg ∈<d and bg ∈< are, weight vector and bias respectively which are
trainable parameters. Miyamoto and Cho (2016) applied this approach at word
level on language model to handle out-of-vocabulary and unusual words [8].
Hashimoto and Tsuruoka (2016) has also applied a very similar approach on
compositional and non-compositional phrase embedding and achieved state-of-
the-art score on verb disambiguation and compositionality detection tasks [9].
The features from the gated word-char layer pass to the dense layer with softmax.
The output is the probability distribution over languages.

                            p = sof tmax(vsT xdt + bs )                         (7)

Where vs and bs are, weight vector and bias respectively which are trainable
parameters.


4     Experiment
4.1   Dataset
We have evaluated our model on the dataset provided by the task organizers.
The dataset contains English comments of Facebook users. English comments
                                  Hybrid gated LSTM-CNN model for INLI         5

are written in native language by users whose native language includes Bengali
(BE), Hindi (HI), Kannada (KA), Malayalam (MA), Tamil (TA) and Telugu
(TE). The statistics of the train dataset are summarized in Table 1. We divided
the training dataset into training and validation data in the ratio of 90:10.


                         Table 1. Training data statistics

                    Language # of Documents in train dataset
                    Bengali              202
                    Hindi                211
                    Kannada              203
                    Malayalam            200
                    Tamil                207
                    Telugu               210




4.2    Model configuration and training

The given dataset is preprocessed to remove multiple occurrences of punctuation
symbols which occurred in between the sentences or at the end of the sentences.
Periods, question marks and exclamation marks are some of the punctuation
symbols that occurred in the available dataset. Punctuation symbols present at
the end of the sentence defines the sentence boundary hence such kinds of re-
peated symbols are replaced by single symbol. There were some occurrences of
emojis which are also removed. After preprocessing the obtained list of words
is lemmatized4 to obtain the root word using the grammatical rules of the con-
cerned language. There is variation in the occurrence of number of sentences and
number of words in a sentence for different language document. To overcome this
issue, we perform padding.
Each word is represented in 100 dimensions vector using pre-trained glove em-
bedding. Glove embedding 5 is trained on 6 billion tokens of the combination of
Gigaword5 + Wikipedia2014 dumps. Some words are not present in glove embed-
dings, which are represented by the glove vector dimension of random uniform
distribution range from -1 to +1. We have set 20 as maximum document length
and 300 as maximum sentence length. Number of hidden units is fixed as 100 for
each directional layer at sentence level and document level. The convolutional
neural network uses three convolutional layer whose filter size is 2, 3 and 5 re-
spectively however stride size is fixed as 1 for all convolutional layer. A dense
layer of the convolutional neural network has 100 hidden units that represents a
document. We have 6 prediction classes (HI, BE, KA, MA, TA, TE), so number
of hidden units in final prediction layer will be based on number of prediction
classes.
4
    https : //www.nltk.org/m odules/nltk/stem/wordnet.html
5
    https://nlp.stanford.edu/projects/glove/
6        Rajesh Kumar Mundotiya et al.

The entire network is trained by Adam optimizer with epoch and mini-batch
size of 15 and 10 respectively. Model is implemented on GeForce 840 GPU using
keras 6 neural network library.


5     Result and Analysis

The task organizers evaluated the model on different metrics such as precision,
recall and F-score for each language with overall accuracy. Two different test
sets are provided for evaluating the model. The number of document in test set1
and test set2 are 783 and 1185 respectively. The obtained result is given in Table
2. We obtain the overall accuracy of 15.3% for test set1 and 17.1% for test set2
Indian native language identification. Our model retrieved more relevant docu-
ments for Tamil language as compared to other languages during testing phase.
For Hindi and Tamil language, our model achieves highest F1-score for Testset1
data.
We obtained accuracy of 88.5% on training dataset however there is significant
drop in the result during the testing phase which indicates over-fitting as a prob-
able reason. There is significant difference in the number of documents shared
for the training and testing phase. Considering the size of trainable data used
during the training phase, our model was unable to predict more generalized fea-
tures due to this large number of random word embeddings were formed which
inturn reflects poor results during the testing phase.


                Table 2. Performance analysis on Testset1 and Testset2

                             Testset1              Testset2
               Class Prec.(%) Rec.(%) F1(%) Prec.(%) Rec.(%) F1(%)
               BE      0.00   0.00    0.00 47.60     4.80    8.80
               HI      30.60  16.30   21.30 12.70    18.80   15.20
               KA      10.40  21.60   14.00 18.00    14.40   16.00
               MA      2.70   2.20    2.40 15.90     13.00   14.30
               TA      14.50  40.00   21.30 12.00    33.60   17.70
               TE      15.00  25.90   19.00 28.40    23.20   25.60
               Overall        15.30                  17.10




6     Conclusion

The purposed hybrid gated LSTM-CNN model for identifying the Indian na-
tive languages, namely Bengali, Hindi, Kannada, Malayalam, Tamil and Telugu,
posted on social media in English by the native speaker of those languages.
6
    https://keras.io/
                                    Hybrid gated LSTM-CNN model for INLI               7

Each document is represented in a vector by combining the output obtained us-
ing bidirectional-LSTM and convolutional neural network. We obtained better
accuracy in test set2 data as compared to the accuracy obtained in the test set1
data. The proposed system can be improved by applying hybrid gated LSTM-
CNN model at word level instead of document level with dropouts.


References
1. Anand Kumar, M., Barathi Ganesh, H.B., Singh, S., Soman, K.P., Rosso, P.
   Overview of the INLI PAN at FIRE-2017 track on Indian native language iden-
   tification (2017) CEUR Workshop Proceedings, 2036, pp. 99-105.
2. Tomokiyo, L. M., & Jones, R. (2001, June). You’re not from’round here, are you?:
   naive Bayes detection of non-native utterance text. In Proceedings of the second
   meeting of the North American Chapter of the Association for Computational Lin-
   guistics on Language technologies (pp. 1-8). Association for Computational Linguis-
   tics.
3. Kochmar, E. (2011). Identification of a writers native language by error analysis
   (Doctoral dissertation, Masters thesis, University of Cambridge).
4. Mechti, S., Abbassi, A., Belguith, L. H., & Faiz, R. (2016, November). An empir-
   ical method using features combination for Arabic native language identification.
   In Computer Systems and Applications (AICCSA), 2016 IEEE/ACS 13th Interna-
   tional Conference of (pp. 1-5). IEEE.
5. Kosmajac, D., & Keselj, V. (2017). DalTeam@ INLI-FIRE-2017: Native Language
   Identification using SVM with SGD Training. In FIRE (Working Notes) (pp. 118-
   122).
6. Vajjala, S., & Banerjee, S. (2017). A study of N-gram and Embedding Representa-
   tions for Native Language Identification. In Proceedings of the 12th Workshop on
   Innovative Use of NLP for Building Educational Applications (pp. 240-248).
7. Malmasi, S., Evanini, K., Cahill, A., Tetreault, J., Pugh, R., Hamill, C., ... & Qian,
   Y. (2017). A report on the 2017 native language identification shared task. In Pro-
   ceedings of the 12th Workshop on Innovative Use of NLP for Building Educational
   Applications (pp. 62-75).
8. Miyamoto, Y., & Cho, K. (2016). Gated word-character recurrent language model.
   arXiv preprint arXiv:1606.01700.
9. Hashimoto, K., & Tsuruoka, Y. (2016). Adaptive joint learning of compositional
   and non-compositional phrase embeddings. arXiv preprint arXiv:1603.06067.
10. Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., & Hovy, E. (2016). Hierarchical at-
   tention networks for document classification. In Proceedings of the 2016 Conference
   of the North American Chapter of the Association for Computational Linguistics:
   Human Language Technologies (pp. 1480-1489).
11. Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv
   preprint arXiv:1408.5882.
12. Anand Kumar M, Barathi Ganesh B and Soman K P. ”Overview of the
   INLI@FIRE-2018 Track on Indian Native Language Identification. In: In workshop
   proceedings of FIRE 2018, FIRE-2018, Gandhinagar, India, December 6-9, CEUR
   Workshop Proceedings.

</pre>