SSNCSE NLP @ T amil NLP - ACL 2022: Transformer based approach for detection of abusive comment for T amil language (2022)

Abstract: Social media platforms along with many other public forums on the Internet have shown a significant rise in the cases of abusive behavior such as Misogynism, Misandry, Homophobia, and Cyberbullying. To tackle these concerns, technologies are being developed and applied, as it is a tedious and time-consuming task to identify, report and block these offenders. Our task was to automate the process of identifying abusive comments and classify them into appropriate categories. The datasets provided by the DravidianLangTech@ACL2022 organizers were a code-mixed form of Tamil text. We trained the datasets using pre-trained transformer models such as BERT,m-BERT, and XLNET and achieved a weighted average of F1 scores of 0.96 for Tamil-English code mixed text and 0.59 for Tamil text.

## introduction abusive comment detection is the method of categorizing and detecting the user-generated offensive comments to any type of insult, vulgarity, or profanity that debases the target @xcite . over the last decade, there has been an exponential growth of user-generated content on social media. given this increase in usage of online platforms, technology must be leveraged in the detection of abusive comments, cyber-bullying, hate speech, and trolling. social media companies have utilized multiple resources to censor comments demeaning others @xcite @xcite ). it's nearly impossible to succeed at perfecting the detector, as a comment's tendency to be abusive depends on the thread of the previous comments @xcite . its subjectivity to the individual and its context-dependent characteristics has been one of the major reasons for its failure. this task aims to train these models to identify abu-sive language that directly targets an individual or a group without bias. our team ssn cse nlp has participated in the shared task of abusive comment detection. to this effect, we were provided with datasets for code mixed tamil text comprising comments from youtube. this poses several challenges due to the low availability of resources for the tamil language. the task focused on the multilingual offensive language detection, categorization of offensive language, and target identification @xcite ; @xcite . we have used pre-trained machine learning transformers like bert,m-bert, and xlnet. in this paper, we investigate the efficacy of different learning models in detecting abusive languages. we then compare the f1-score of the different transformer models for both datasets. tamil is one of the world's longest-surviving classical languages (anita and @xcite @xcite . according to a. k. ramanujan, it is "the only language of modern india that is recognizably continuous with a classical history." because of the range and quality of ancient tamil literature, it has been referred to as "one of the world's major classical traditions and literatures." for about 2600 years, there has been a recorded tamil literature. the earliest period of tamil literature, known as sangam literature, is said to have lasted from from 600 bc to ad 300 @xcite @xcite . among dravidian languages, it possesses the oldest existing literature. the earliest epigraphic documents discovered on rock edicts and "hero stones" date from the 6th century bc @xcite @xcite . the remainder of the paper is organized into 5 sections. section 2 discusses the related works in the field of artificial intelligence, on abusive com-ment detection, for both tamil and other languages. the methodology proposed for the model along with the models implemented are elaborately explained in the 3rd section of this paper. in section 4 the results and the observations are discussed. section 5 concludes the paper. ## related works a lot of research is being done on detecting offensive language from social media platforms in the field of artificial intelligence and natural language processing @xcite @xcite @xcite @xcite . in this section, we will be reviewing the research work. the authors of @xcite has analyzed various techniques and neural network models to detect offensive language in code-mixed romanized social media text in tamil. they have implemented selective translation and transliteration for text conversion in romanized and code-mixed settings, and are positive that this can be extended to romanized and code-mixed contexts of other languages. the authors of b and a (2021b) identified offensive language content of the code-mixed dataset of comments/posts in dravidian languages (tamil-english, malayalam-english, and kannada-english) collected from social media. the basic tfidf and count vectorizer features were found to perform best when compared to sentence embeddings. they detected that machine learning models are giving better performance than deep learning models. a large transliterated bengali corpus sazzed (2021) introduced, consisting of 3000 comments collected from youtube, which are manually annotated into abusive and non-abusive categories. the comparative performances of various supervised ml and deep learning-based classifiers are given, and bilstm, the deep learning-based architecture, obtains a relatively lower f1 score compared to lr and svm, which could be attributed to the small size (i.e.,3000 comments) of the corpus. the authors of @xcite emphasized on improving offensive language identification by prioritizing the construction of a bigger dataset and generating pseudo-labels on the transliterated dataset, combining the latter with the former to a three-level classification system with naive bayes classifier in the first level, multinomial updatable naive bayes in the second level, and a rulebased classifier named dtnb in the third level is used in @xcite . the authors @xcite reported the lexical features, static and deep contextualized embedding for the support vector machine classifiers to detect arabic offensive language and also determined the topics, dialects, and genders which are associated with the offensive tweets. the addition of the sentiment and contextual features provide significantly improved performance to a basic tfidf model in @xcite . the authors of @xcite proposed an approach based on graph convolutional networks to show that author profiles that directly capture the linguistic behavior of authors along with the structural traits of their community significantly advance the current state of the art. the authors of @xcite shows an approach that outperforms all other models and has achieved better performance in classifying short messages. the approach taken did not rely on pretrained vectors, which provides a serious advantage when dealing with short messages. ## methodology and data pre-processing in this section, we have illustrated our implementation of the pre-trained machine learning transformer models in detail. further, we will investigate the performance of the various transformer models in the coming sections. the architecture of the proposed model and the steps are given below 1. the dataset provided by the lt-edi 2021 priyadharshini et al. (2022) for the tamil, and code-mixed tamil text consisted of 3499, and 9293 youtube comments respectively. the details are given in ## conclusions in this paper, we have investigated the baseline accuracy of different models as well as their variants on the test datasets. there is an increase in demand for abusive language identification on social media, and the goal of this task was to detect whether the comment contains abusive language or not. our team had secured the 8th rank in the shared task for the code-mixed tamil dataset, and the 12th rank for the tamil dataset. our models performed the baseline for both the tasks but performance can further be improved by adopting favorable features. references r anita and cn subalalitha. 2019a. an approach to cluster tamil literatures using discourse connectives. in 2019 ieee 1st international conference on energy, systems and information processing (icesip), pages 1-4. ieee. r anita and cn subalalitha. 2019b. building discourse parser for thirukkural. in proceedings of the 16th international conference on natural language processing, pages 18-25. bharathi b and agnusimmaculate silvia a. 2021a. ssncse nlp@dravidianlangtech-eacl2021: meme classification for tamil using machine learning approach. in proceedings of the first workshop on speech and language technologies for dravidian languages, pages 336-339, kyiv. association for computational linguistics. bharathi b and agnusimmaculate silvia a. 2021b. ss-ncse nlp@dravidianlangtech-eacl2021: offensive language identification on multilingual code mixing text. in proceedings of the first workshop on speech and language technologies for dravidian languages, pages 313-318, kyiv. association for computational linguistics. 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.