MUCIC @ T amil NLP - ACL 2022: Abusive Comment Detection in T amil Language using 1 D Conv- LSTM (2022)

Abstract: Abusive language content such as hate speech, profanity, and cyberbullying etc., which is common in online platforms is creating lot of problems to the users as well as policy makers. Hence, detection of such abusive language in user-generated online content has become increasingly important over the past few years. Online platforms strive hard to moderate the abusive content to reduce societal harm, comply with laws, and create a more inclusive environment for their users. In spite of various methods to automatically detect abusive languages in online platforms, the problem still persists. To address the automatic detection of abusive languages in online platforms, this paper describes the models submitted by our team - MUCIC to the shared task on “Abusive Comment Detection in Tamil-ACL 2022”. This shared task addresses the abusive comment detection in native Tamil script texts and code-mixed Tamil texts. To address this challenge, two models: i) n-gram-Multilayer Perceptron (n-gram-MLP) model utilizing MLP classifier fed with char-n gram features and ii) 1D Convolutional Long Short-Term Memory (1D Conv-LSTM) model, were submitted. The n-gram-MLP model fared well among these two models with weighted F1-scores of 0.560 and 0.430 for code-mixed Tamil and native Tamil script texts, respectively. This work may be reproduced using the code available in https://github.com/anushamdgowda/abusive-detection .

## introduction abusive language refers to the usage of words for any type of insult, vulgarity, profanity, sexism, or misogyny @xcite that debases the target, as well as anything that causes aggravation @xcite . the term abusive language is often reframed as offensive language @xcite and hate speech @xcite . in recent years, an increasing number of users have witnessed the offensive behav-1 https://github.com/anushamdgowda/abusive-detection ior on social media @xcite targeting individuals, group or community. in spite of many social media companies using a variety of tools such as human reviewers, user reporting procedures, etc., to censor the offensive language, the problem is growing day by day mainly because the offensive/abusive language detection algorithms fail to capture the subject and context-dependent characteristics of the text @xcite @xcite . for example, an individual message may appear harmless, but when viewed in the context of previous threads, it may appear abusive, and vice versa. it is challenging even for human beings to detect such abusive language. social media texts are usually written mixing regional languages such as tamil, kannada, malayalam, etc., with english at sub-word, word or sentence level @xcite @xcite @xcite . further, the usage of internet slangs, words in short forms, words of other languages, emojis etc., adds to the problem of tackling abusive language @xcite . the focus of abusive comment detection algorithms on low-resources like tamil is rarely explored due to scarcity and unavailability of annotated dataset @xcite . the rest of the paper is arranged as follows: a review of related work is included in section 2, and the methodology is discussed in section 3. experiments, and results are described in section 4 followed by concluding the paper with future work in section 5. ## related work most of the abusive comment detection works focus on high-resource languages like english, leaving the low-resource languages such as dravidian languages, arabic, persian, urdu, etc., unexplored for the task @xcite . a brief description of some of the recent abusive language detection works are given below: the main problem with low-resource languages are the annotated datasets for abusive language detection. even human annotators find it difficult to annotate some of the comments as abusive because of which building a large and reliable dataset becomes challenging. @xcite found that datasets openly available for abusive language detection on twitter ranged from 10k to 35k in size and are insufficient to train deep learning (dl) models. @xcite explored abusive comment detection in youtube comments using several machine learning (ml) and dl models as baselines and used n-grams features and pre-trained glove embeddings to train ml and dl models respectively. ada-boost (ml model) and 1-dimensional convolutional neural network (1d-cnn) (dl model) models obtained 87.29 and 89.24 f1-scores on comments without replies. adding replies as conversational context enhanced the results to 91.96 and 91.68 f1-scores for ada-boost and 1d-cnn respectively. lee et al. (2018 ) compared various learning models using hate and abusive speech twitter dataset @xcite . in addition to traditional ml approaches (nb, lr, svm, and rf), they also investigated neural network (nn) models (cnn, recurrent neural networks (rnn) and bidirectional gated recurrent unit (bigru)). term frequency-inverse document frequency (tf-idf) of word vectors and pre-trained glove vectors were used to train ml and nn models. further, latent topic clustering (ltc) which extracts latent topic infor-mation from the hidden states of rnn is used as additional information in classifying the text data. bigru model based on word features and ltc out performed the other models with an f1-score of 0.805. eshan and hasan (2017) experimented tf-idf of unigram, bigram, and trigram features to train ml algorithms (rf, multinomial nb, svm with linear, radial basis function, polynomial, and sigmoid kernels) and evaluated facebook dataset of bengali abusive text. svm with linear kernel and trigram feature achieved the best accuracy of 76% accuracy among all the models. ml (linear support vector classifier (lin-earsvc), lr, mnb, rf) and dl (rnn with long short term memory (lstm)) algorithms, were used to detect multi-type abusive bengali text by @xcite . linearsvc, lr, and mnb models were trained with filtered non-bengali data transformed to vectors using a countvectorizer foot_1 . and rf classifier was trained with the tf-idf vectors obtained after filtering punctuation, numerals, and emotions. for dl model, the raw dataset is stemmed and word embedding is utilized to encode the text. rnn with lstm outperforms other algorithms with the highest accuracy of 82.20%. several code-mixed tamil datasets are used in various shared tasks, such as sentiment analysis in tamil (chakravarthi et al., 2020), hate speech detection in dravidian languages @xcite , hope speech detection (chakravarthi, 2020), offensive language identification (oli) in dravidian languages, @xcite , etc. since code-mixed texts do not follow any grammar, @xcite proposed a learning model using sub-words generated by char sequences to deal with code-mixed texts for the task of oli in dravidian languages @xcite . they used word n-grams with sub-words and a majority voting classifier with extreme gradient boosting (xgb), lr, and mlp estimators and obtained a weighted average f1-score of 0.75. in another experiment on code-mixed tamil texts, @xcite combined char sequences with syntactic bi-grams and tri-grams for hope speech detection task (chakravarthi, 2020) and fed a voting classifier with three ml estimators, namely: lr, xgb and mlp. the authors created a code-mixed bert language model from figure 2: framework of 1d conv-lstm model scratch and obtained an average weighted f1-score of 0.54. however, in this study, the best performance was that of hard voting classifier with an average weighted f1-score of 0.59 that secured third rank in the competition. ## methodology the first step in processing text data is to clean the text by removing the punctuation symbols, numerical data, frequently occurring words, and stopwords, as these features do not help in identifying the abusive content. clean data is expected to improve the performance of the learning models. two models: i) n-gram-mlp trained with char n-grams and ii) 1d conv-lstm model, were proposed to identify the abusive comment from native tamil script and code-mixed tamil texts. the framework of the proposed models are shown in figure 1 and 2 and explanation of the models follows: ## experiments and results the datasets provided by the shared task organizers contains native tamil script (tamil) and code-mixed tamil (ta-en) texts and the task is to classify the input text into different categories as shown in the unlabeled test sets shared by the organizers were used to evaluate the proposed models and the predictions were submitted to the organizers for final evaluation and ranking. as per the results in the final leaderboard of the shared task, the proposed n-gram-mlp model obtained average weighted f1scores of 0.560 and 0.430 for tamil and ta-en texts respectively. results of the proposed models on development set and test set are shown in ## conclusion this paper describes the participation of our team mucic in "abusive comment detection in tamil-acl 2022" shared task. the objective of this shared task is to identify the different categories of cohope-hope speech detection for equality, diversity, and inclusion in code-mixed texts. in proceedings of the first workshop on language technology for equality, diversity and inclusion, pages 180-187, kyiv. association for computational linguistics. fazlourrahman balouchzahi and h l shashirekha. 2021. la-saco: a study of learning approaches for sentiments analysis in code-mixing texts. in proceedings of the first workshop on speech and language technologies for dravidian languages, pages 109-118, kyiv. association for computational linguistics. fazlourrahman balouchzahi and hl shashirekha. 2020. puner-parsi ulmfit for named-entity recognition in persian texts. in congress on intelligent systems, pages 75-88. springer. b bharathi, bharathi raja chakravarthi, subalalitha chinnaudayar navaneethakrishnan, n sripriya, arunaggiri pandian, and swetha valli. 2022. findings of the shared task on speech recognition for vulnerable individuals in tamil. in proceedings of the second workshop on language technology for equality, diversity and inclusion. association for computational linguistics.