=Paper=
{{Paper
|id=Vol-3681/T5-7
|storemode=property
|title=Learning Models with Text Augmentation for Sarcasm Detection in Malayalam and Tamil Code-mixed Texts
|pdfUrl=https://ceur-ws.org/Vol-3681/T5-7.pdf
|volume=Vol-3681
|authors=Navya N,Vanitha V,Asha Hegde,H L Shashirekha
|dblpUrl=https://dblp.org/rec/conf/fire/NVHS23
}}
==Learning Models with Text Augmentation for Sarcasm Detection in Malayalam and Tamil Code-mixed Texts==
https://ceur-ws.org/Vol-3681/T5-7.pdf
Learning Models with Text Augmentation for Sarcasm
Detection in Malayalam and Tamil Code-mixed Texts
Navya N, Vanitha V, Asha Hegde and H L Shashirekha
Department of Computer Science, Mangalore University, Mangalore, Karnataka, India
Abstract
Users can express their sentiments, sarcasm, emotions, signs of depression, and hatred comments on social
media platforms leading to user-generated text. Sarcasm is a form of linguistic expression that conveys a
message which is opposite to the intended message, that is been typically used to mock or humorously
criticize. If the sarcastic messages crosses the boundary, they can hurt an individual/community spoiling
the healthy environment of social media platforms. The social media text usually will be in code-mixed
format and sarcastic comments which are not excluded from this contributes to the complexities of
processing code-mixed sarcastic comments. Further, in parallel with user-generated content on social
media platforms, sarcastic comments also have increased making it difficult to detect them manually.
Hence, there is a demand for tools/models that could automatically identify such code-mixed sarcastic
comments on social media to keep the social media platforms healthy. In this paper, we - team MUCS,
describe three distinct binary classification models: i) Ensemble of Machine Learning (ML) classifiers
(Logistic Regression (LR), Random Forest Classifier (RF), Support Vector Classifier (SVC))) with hard
voting, Deep Learning (DL) models (Convolutional Neural Network (CNN)), and Transfer Learning
(TL) based models (Mutlilingual Bidirectional Encoder Representations from Transformers (mBert)
and Distilled version of Mutlilingual Bert (mDistilBert) for Malayalam and Tamil code-mixed texts
respectively), submitted to the shared task ”Sarcasm Identification of Dravidian Languages (Malayalam
and Tamil)” at DravidianCodeMix 2023 @Forum for Information Retrieval Evaluation (FIRE) 2023. As
the given dataset is imbalanced, Text Augmentation (TA) techniques are explored to balance the dataset.
Among the proposed models, Ensemble model obtained macro F1 scores of 0.71 and 0.70 securing 4th
and 5th ranks for Malayalam and Tamil code-mixed texts respectively.
Keywords
Code-mixed, Machine Learning, Deep Learning, Transfer Learning, Text Augmentation
1. Introduction
In the internet era, social media platforms play a significant role in facilitating the sharing of
user thoughts, reviews, and opinions. However, the anonymity on these platforms often leads to
the presence of hate speech, offensive language, and sarcasm, in user-generated text targeting
an individual or a group. Sarcasm, in particular, is a common way to mock or ridicule something
or someone in which the face value of the text is just opposite to its intended meaning. For e.g.,
a comment like ”Very good; well done!” for a bad situation or when something has really went
wrong, depicts a sarcastic comment. The face value of this comment says someone has done
a good job, however, the subtle meaning of this comment speaks exactly opposite to the face
Forum for Information Retrieval Evaluation, December 15-18, 2023, India
Envelope-Open navyabangera451@gmail.com (N. N); vjvanitha001@gmail.com (V. V); hegdekasha@gmail.com (A. Hegde);
hlsrekha@mangaloreuniversity.ac.in (H. L. Shashirekha)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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value of the comment. As sarcastic comments may hurt the individuals/communities and spoil
the healthy social media environment, detecting sarcastic content on social media has to be
given at most priority. Sarcasm detection is the essential process of recognizing and flagging
instances of sarcasm in social media texts, helping to moderate the content to have a respectful
and safe online environment.
Social media text is often a collection of very informal user-generated text exhibiting code-
mixing, especially in multilingual countries like India, where people commonly blend their
mother tongue or local language with English when posting comments on social media platforms
[1]. Code-mixing, as a linguistic phenomenon, entails the incorporation of multiple languages
within a single sentence, word, or even at a sub-word level [2, 3]. This practice often stems
from technological constraints that favor the use of the Roman script, making it easier for users
to express their sentiments and opinions in their native languages while incorporating English
terms. The convenience of keying in Roman letters is evident, as it avoids the complexities
associated with using native language scripts, especially in the context of Indian languages that
exhibit complex key combinations [4]. Further, the informal nature of social media text allows
incomplete sentences or words, user-defined abbreviations, words with recurring characters,
slangs, suffixes from other languages, etc., which varies from one user to another. This variation
in user-generated text makes it very challenging to process them.
Tamil is a prominent Dravidian language spoken by Tamil people in India, Sri Lanka, and
worldwide by the Tamil diaspora [5]. It has official recognition in India, Sri Lanka, and Singapore.
Tamil script which evolved from the Tamili script, Vatteluttu alphabet, and Chola-Pallava script
[6] comprises of 12 vowels, 18 consonants, and 1 āytam (voiceless velar fricative). It is also used
for writing the minority languages - Saurashtra, Badaga, Irula, and Paniya. Malayalam, on the
other hand, is a Dravidian language spoken mainly in Kerala, India. It uses an alpha-syllabic
script that is part of the abugida family of writing systems, combining alphabetic and syllable-
based elements [7]. Tamil/Malayalam speaking people, especially the younger generation who
are active on social media use a combination of Tamil/Malayalam and English words to posts
the comments using a combination of native and Roman script leading to code-mixed data in
multiple language scripts [8].
Indian languages in general, and Dravidian languages like Tamil and Malayalam in particular,
are considered as low-resource languages because of which data collection and annotation for
any application becomes difficult [9]. The collection and annotation of code-mixed data for any
application is intensified by the scarcity of resources. Further, the algorithms which process
mono-lingual texts may not perform better for code-mixed texts. Hence, detecting sarcasm in
code-mixed texts necessitates the development of multilingual language models and algorithms
that help to process the code-mixed text which exhibits linguistic variations of more than one
languages [10].
To address the challenges of processing code-mixed texts in Tamil and Malayalam for sarcasm
identification, in this paper, we - team MUCS, describe the models submitted to the shared task
”Sarcasm Identification of Dravidian Languages (Malayalam and Tamil)” in DravidianCodeMix
@FIRE 2023 [11]. Sarcasm identification problem is modeled as a binary classification task,
to identify the given Malayalam and Tamil text as either ’Sarcastic’ or ’Non-sarcastic’. Three
distinct binary classification models: i) Ensemble of ML classifiers (LR, RF, and SVC) trained
with Term Frequency-Inverse Document Frequency (TF-IDF) of syllable n-grams ii) DL based
model (CNN trained with Keras embeddings), and iii) TL based models (transformer model
trained with mBert and mDistilBert for Malayalam and Tamil respectively), are proposed to
identify sarcasm in the given Malayalam and Tamil texts. [12]. As the given datasets are
imbalanced, Text Augmentation (TA) approaches are explored to balance the Train set with the
aim of improving the performance of the classifiers. Sample Tamil and Malayalam comments
from the dataset along with their English translations are shown in Table 1.
Table 1
Sample Tamil and Malayalam comments from the dataset along with their English translations
The rest of the paper is organized as follows: Section 2 contains related work, Section 3
describes the methodology and Section 4 describes experiments and results followed by the
conclusion and future work in Section 5.
2. Related Work
In spite of the availability of several models for sarcasm detection in code-mixed texts in some
Indian languages [13], code-mixed texts in Dravidian languages are not yet explored fully in
this direction and few of the relevant works are described below:
Kalaivani and Thenmozhi [14] implemented several ML models, DL model (Recurrent Neural
Network with Long Short Term Memory (RNN-LSTM)) and TL-based model (transformer based
classifier with BERT), for identifying sarcasm in English text obtained from Twitter and Reddit
forums. They trained ML models with Doc2Vec vectors and TF-IDF of word unigrams, DL model
with Keras embeddings and TL based model with BERT features. Among their proposed models,
TL based BERT model obtained better F1 scores of 0.722 and 0.679 for the Twitter and Reddit
forums respectively. Patil, Pravin K and Kolhe, SR [15] created manually annotated MarathiSarc
- a Marathi dataset with 2,400 Marathi tweets for identifying sarcasm and implemented many
ML models trained with TF-IDF of word unigrams. Among the ML models they experimented,
XGBoost model outperformed the other models with a macro F1 score of 0.65. Pandey and
Singh [16] trained several ML classifiers with TF-IDF of word unigrams, DL models (Deep
Neural Network (DNN) CNN, LSTM) with keras embeddings, and TL-based model with BERT,
for identifying sarcasm in code-mixed Hindi text. Further, they implemented a hybrid model
by stacking LSTM network at the final layer of BERT model and their hybrid model obtained
a remarkable macro F1 score of 0.98. Kumar et al. [17] implemented a DL model called sAtt-
BiLSTM convNet - a soft attention-based bidirectional LSTM model stacked on CNN to detect
sarcasm in English text. Using Global Vectors (GLoVe) for semantic representation of words,
their proposed model achieved a remarkable accuracy of 97.87%.
The dataset may be imbalanced and learning models trained on this imbalanced dataset may
give results favoring the majority class, affecting the performance of the classifier. Several
researchers have addressed the issue of data imbalance and some of the prominent ones are
described below:
Abdullah et al. [18] highlighted the significance of resampling techniques to address data
imbalance issues by adjusting the proportion of majority and minority instances either by over-
sampling or under-sampling. Lee et al. [19] explored back-translation using Google translate1
for TA to address the data imbalance issue for sarcasm detection in English text. A fine-tuned
mBert model is presented by Kalaivani and Thenmozhi [20] for sentiment analysis in code-mixed
Tamil, Malayalam and Kannada texts. As the given dataset is imbalanced, they augmented the
Train set by employing transliteration and translation techniques and their proposed models
obtained macro F1 scores of 0.603, 0.698, and 0.595 for Tamil, Malayalam, and Kannada texts
respectively.
Ensemble approaches have shown improved performance in many text classification applica-
tions such as sentiment analysis, sarcasm detection, hate speech detection, etc. An ensemble
of ML classifiers (SVC, LR, and RF) with soft voting considering TF-IDF of character n-grams
features in the range (1, 3) is presented by Kumar et al. [21] for sentiment analysis in code-mixed
Kannada, Malayalam, and Tamil texts. Their proposed models exhibited weighted F1 scores of
0.63, 0.73, and 0.62 for Kannada, Malayalam, and Tamil code-mixed texts respectively.
The related work highlights the research on sarcasm identification available in few Indian
languages ensuring the need to develop efficient tools/models for sarcasm detection in other
languages including the Dravidian languages Tamil and Malayalam.
Figure 1: Framework of the proposed methodology
1
https://translate.google.co.in/
Table 2
Distribution of Tamil and Malayalam code-mixed dataset
Tamil Malayalam
Labels Train set Dev set Train set Dev set
Non_sarcastic 19,866 4,939 9,798 2,427
Sarcastic 7,170 1,820 2,259 588
Total 27,036 6,759 12,057 3,015
3. Methodology
The proposed methodology aims to identify sarcasm in Malayalam and Tamil code-mixed texts.
Framework of proposed methodology is shown in Figure 1 and the components involved in the
methodology are described below:
3.1. Text Augmentation
The datasets provided by the organizers for the shared task exhibit significant class imbalance
as shown in Table 2. This data imbalance may affect the performance of the learning models as
the training set is biased. Balancing the dataset either by the resampling techniques or by TA
approaches can resolve the data imbalance issue. While resampling techniques increase the size
of the minority class by replicating the text, TA approaches increase the size of the minority
class by generating the diversified synthetic data. These approaches expand the training data
to improve performance of the learning models for the Natural Language Processing (NLP)
tasks, such as machine translation [22], text classification, question and answering system, etc.,
[23]. The statistics of the augmented datasets are shown in Table 3 and TA techniques used to
increase the text belonging to minority class (’Sarastic’) to balance the dataset are explained
below:
1. Back-translation - is a technique where sentences from one language are translated into
another language and then translated back to the original language. This technique
generates textual data of distinct words for the original text while preserving the original
context and meaning allowing a simple augmentation of text. Tamil and Malayalam text
labeled as ’Sarcastic’ is back-translated using Google translate for augmentation.
2. Prompt-based ChatGPT - has gained popularity in text generation [24]. Prompts serve as
the primary means of interacting with ChatGPT that enables users to request information,
generate content, or engage in conversations for a wide range of tasks including text
generation, information retrieval, and translation. This work utilizes the prompt-based
ChatGPT to generate synthetic data to increase the number of comments in Tamil dataset
labeled as ’Sarcastic’.
3. Augmentation using the given dataset - the proposed models incorporate either language
independent techniques (TF-IDF of syllable n-grams and keras embeddings) or multilin-
gual models (mBert/mDistilBert pre-trained models) to obtain the features. Hence, adding
the data of the same class from another dataset will augment the existing dataset. This is
Table 3
Statistics of Tamil and Malayalam comments belonging to ’Sarcastic’ class after TA
TA Techniques Tamil Malayalam
Back-translation 2,251 2,251
Prompt based ChatGPT 1,339 -
Augmentation using given dataset 2,259 5,292
Total sarcastic comments after TA 5,849 7,543
Total sarcastic comments given in the dataset 7,170 2,259
Total sarcastic comments in the augmented dataset 13,019 9,802
carried out by adding Tamil sarcastic comments to Malayalam dataset and vice versa, to
augment the given dataset.
3.2. Preprocessing
The objective of preprocessing is to clean the text with the intention of improving the accuracy
of the learning models. In this direction, emojis are converted into text using demoji2 library
and punctuation, digits, and URLs are removed from the given Malayalam and Tamil code-mixed
texts. English and Tamil stopwords available at Natural Language Toolkit (NLTK)3 and github4
are used as references to remove English and Tamil stopwords respectively, as they will not
contribute to the sarcasm detection task.
3.3. Model Description
The preprocessed text is used to construct three distinct binary classification models: i) Ensemble
of ML models, ii) DL model, and iii) TL-based models. The description of these models is given
belows:
3.3.1. Ensemble of ML Models
Ensemble model consists of Feature Extraction and Classifier Construction as described below:
Feature Extraction - TF-IDF is a normalized representation of text documents used to reduce
the impact of frequently occurring words across the documents. These vectors help to prior-
itize words that are distinctive to a document, making them valuable for various NLP tasks
such as document retrieval, text classification, information retrieval etc. Syllables are distinct
units of pronunciation with a single vowel sound. This representation is helpful in processing
the text with non-romanized scripts as it provides meaningful tokens. In this work, syllable
n-grams in the range (1, 3) are obtained from the preprocessed data and are vectorized using
TfidfVectorizer5 . Table 4 shows the sample Tamil and Malayalam code-mixed comments with
their syllables and syllable unigrams, bigrams and trigrams.
2
https://pypi.org/project/demoji/
3
https://pythonspot.com/nltk-stop-words/
4
https://gist.github.com/arulrajnet/e82a5a331f78a5cc9b6d372df13a919c
5
https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html
Classifier Construction - an ensemble method is a way of creating a new classifier by combin-
ing several diversified baseline classifiers such that the weakness of one classifier is overcome
by the strength of another classifier. The idea behind ensemble approach is to consider a
classifier combination as one single classifier and to improve its performance as compared to
the performance of individual classifiers.
Table 4
Sample Tamil and Malayalam code-mixed comments with their syllables
Since the ensemble model incorporates multiple classifiers, it relies on a voting mechanism
to predict class labels for unlabeled samples and hence, this approach is also known as a
voting classifier. It allows the ensemble model to make accurate predictions for the new
unlabeled samples by aggregating the decisions of individual classifiers. An ensemble of three
ML classifiers, namely: LR, SVC, and RF, with hard voting is employed to detect sarcasm in the
given texts.
• Logistic Regression - combines features linearly and uses regularization techniques to
prevent over-fitting and the logistic function to classify instances into one of the predefined
classes [25].
• Support Vector Classifier - excels in identifying intricate, non-linear relationships among
the features, making it highly accurate in categorizing text documents [26].
• Random Forest - makes use of ensemble learning by combining a number of decision tree
classifiers creating a ”forest” that is trained via bagging or bootstrap aggregation [27].
The hyperparameters of the ML classifiers are used with their default values.
3.3.2. Deep Learning Model
CNN architecture is employed to perform sarcasm identification in Malayalam and Tamil
code-mixed texts. The model includes an embedding layer obtained from keras embeddings
with a vocabulary of size 35,000 and embedding dimension 1000. It includes a convolutional
layer featuring 64 filters and a kernel of size 2. For downsampling, the approach employs max
pooling and subsequently the feature maps are transformed into a flattened representation as a
one-dimensional vector. Eventually, an LSTM layer with 100 units is stacked with the previous
layers to capture long-range dependencies and improving the model’s ability to understand the
context and meaning of the text. The final classification probabilities are generated through a
dense layer employing the softmax activation function [28].
Table 5
Hyperparameters and their values used in mBert and mDistilBERT
Hyperparameters mBert mDistilBert
Layers 12 6
Dimensions 768 768
Attention heads 12 12
Learning Rate 1e-5 2e-5
Batch Size 32 16
Maximum Sequence Length 128 512
Dropout 0.3 0.3
3.3.3. Transfer Learning-based Models
TL is a learning approach where the knowledge acquired in training a source model is used
to build another but related target model. Rather than building a model from scratch, this
process accelerates learning and enhances the effectiveness of the target model by utilizing
the pre-trained knowledge from the source task. This study makes use of two pre-trained
models: mBert and mDistilBert, for Malayalam and Tamil code-mixed texts respectively, to
detect sarcasm. Descriptions about mBert and mDistilBert models are as follows:
• mBert - is a BERT variant pre-trained on a vast amount of text data encompassing over
104 languages including Tamil, Malayalam and English in their native scripts, making it
a multilingual language model by capturing and encoding semantic information from
diverse linguistic contexts. It provides tokenizers and pre-trained embeddings for each
token.
• mDistilBert - is a distilled version of BERT model pre-trained on a vast amount of text
data encompassing over 104 languages including Malayalam and Tamil text extracted
from Wikipedia along with their native and romanized scripts.
In this work, mBert model is fine-tuned on the Malayalam dataset, as majority of the comments
in this dataset are in its native script along with the English text in Roman script and mDistilBert
is fine-tuned on Tamil dataset since most of the comments are romanized. Hyperparameters
and their values used to configure mBert and mDistilBert models are shown in Table 5.
4. Experiments and Results
The datasets provided for the shared task encompasses YouTube comments for both Malayalam
and Tamil code-mixed texts. The majority of the comments are composed in native or Roman
scripts, featuring code-mixed Tamil/Malayalam text with English.
Several experiments are conducted with different learning approaches and the models which
gave the better results on the Development (Dev) set are evaluated on the Test set to get the
predictions. The predicted labels of the Test set are evaluated by the organizers based on macro
F1 scores and the performance of the proposed models on the Development and Test set are
shown in the Table 6. From the table, it is clear that ensemble of ML classifiers performed
Table 6
Results of the proposed models in terms of macro F1 score
Malayalam Tamil
Models TA techniques Dev set Test set TA techniques Dev set Test set
Original data 0.68 0.67
Original data 0.68 0.68 Original data
0.66 0.67
+Back translation
Original data
Original data
0.71 0.71 +Back translation 0.70 0.68
+Back translation
+ChatGPT
Ensemble Original data
Original data
+Back translation
+Back translation 0.71 0.71 0.71 0.70
+ChatGPT
+Tamil
+Malayalam
Original data 0.50 0.45
Original data 0.71 0.50 Original data
0.50 0.45
+Back translation
Original data
Original data
0.69 0.50 +Back translation 0.50 0.45
+Back translation
+ChatGPT
CNN Original data
Original data
+Back translation
+Back translation 0.68 0.64 0.50 0.45
+ChatGPT
+Tamil
+Malayalam
Original data 0.71 0.69
Original data 0.45 0.45 Original data
0.68 0.68
+Back translation
Original data
Original data
0.69 0.69 +Back translation 0.70 0.69
+Back translation
+ChatGPT
mBert/
Original data
mDistilBert Original data
+Back translation
+Back translation 0.68 0.64 0.70 0.69
+ChatGPT
+Tamil
+Malayalam
better with macro F1 scores of 0.71 and 0.70 securing 4th and 5th ranks for Malayalam and
Tamil code-mixed texts respectively. This may be due to utilization of syllable n-grams features,
which capture the meaningful tokens, particularly in processing non-roman script languages.
Additionally, the pretrained models employed in this study have not effectively captured the
sarcastic language nuances due to the limitations of the training data. The proposed models
have shown improved macro F1 scores after TA except for Tamil code-mixed data which shows
no improvement at all using CNN model.
5. Conclusion
This paper describes three distinct binary classification models: i) Ensemble of ML classifiers
(LR, RF, and SVC) trained with TF-IDF of syllable n-grams in the range (1, 3), ii) DL model (CNN
trained on Keras embeddings), and iii) TL based models (transformer model trained with mBert
and mDistilBert for Malayalam and Tamil code-mixed texts respectively), submitted to ”Sarcasm
Identification of Dravidian Languages (Malayalam and Tamil)” in DravidianCodeMix@FIRE
2023 shared task. As the given dataset is imbalanced, TA techniques are employed to augment
the data and the augmented data is used to train the proposed models. The proposed ensemble
model exhibited macro F1 scores of 0.71 and 0.70 securing 4th and 5th ranks for Malayalam and
Tamil code-mixed texts respectively.
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