IIITDWD @ T amil NLP - ACL 2022: Transformer-based approach to classify abusive content in D ravidian Code-mixed text (2022)

Abstract: Identifying abusive content or hate speech in social media text has raised the research community’s interest in recent times. The major driving force behind this is the widespread use of social media websites. Further, it also leads to identifying abusive content in low-resource regional languages, which is an important research problem in computational linguistics. As part of ACL-2022, organizers of DravidianLangTech@ACL 2022 have released a shared task on abusive category identification in Tamil and Tamil-English code-mixed text to encourage further research on offensive content identification in low-resource Indic languages. This paper presents the working notes for the model submitted by IIITDWD at DravidianLangTech@ACL 2022. Our team competed in Sub-Task B and finished in 9th place among the participating teams. In our proposed approach, we used a pre-trained transformer model such as Indic-bert for feature extraction, and on top of that, SVM classifier is used for stance detection. Further, our model achieved 62 % accuracy on code-mixed Tamil-English text.

## introduction many people from various demographics and linguistic backgrounds have been using social media sites to exchange information and interact with others. further, these speakers tend to combine their mother tongue with a second language during the conversation. this leads to code-mixed text; codemixing refers to two or more languages appearing one after another during a conversation @xcite . monitoring code-mixed content from social media sites has caught the research community's interest in natural language processing. currently, many social media networks use a manual content screening method to deal with abusive content posted by the users. here a human reviewer will go through the user posts and determine whether they violate the norms or not @xcite . however, as the number of social media users grows, massive amounts of data are generated, making it virtually difficult to monitor every data point personally. as a result, the manual technique of dealing with abusive content has become unsuccessful. further, it increased the demand for automated abusive language identification models in a social media text, which is largely code-mixed. the existing models have been trained on highresource monolingual languages such as english and hindi. further, due to the complexity induced by code-mixing at different language levels, models trained with monolingual text failed to identify objectionable features in the code-mixed text. as a result, identifying abusive content in indic languages poses a significantly greater problem for the nlp community. hence identifying abusive content in low-resource dravidian languages such as tamil, kannada, and malayalam is made more difficult due to a lack of pre-trained models and a scarcity of training data to further train models. to bridge this gap, dravidianlangtech@acl 2022 @xcite organizers have provided a gold standard data set for abusive content identification in dravidian languages such as tamil and tanglish code-mixed text. the task's objective is to identify abusive categories from youtube comments at the sentence/comment level. the original task is divided into two sub-tasks: sub-task a involves sentence level abusive category detection from monolingual tamil script, and sub-task b involves comment level abusive category identification from code-mixed tamil-english text. our team has participated in sub-task b and secured 9 th rank among the participating teams, and this paper presents working notes of our presented model. the remainder of the paper is organized as follows: section 2 provides a review of existing work, section 3 provides insight into the suggested model, and section 4 concludes by offering information about outcomes. ## litrature review the subject of automatic detection of hostile and harmful information from social media has attracted the interest of many researchers and practitioners from industry and academia. however, most of the past research has focused on highresource languages. previous attempts have been made to develop hate speech detection models in english, german @xcite , and italian @xcite . but on the other hand, low-resource indic languages are rarely explored. the first such attempt was made by @xcite , they have created annotated corpus in hindi-english code mixed text. they used traditional machine learning models to classify features extracted from the data set, such as character n-grams, word n-grams, punctuation, negation words, and hate lexicons. @xcite used a cnn-based transfer learning approach to detect abusive tweets in hindi-english code-mixed text. they also introduced the heot data set and the profanity lexicon set. abusive content identification in indic languages is also the topic of a few shared tasks. chakravarthi et al. created a shared task in low resource code-mixed dravidian languages like tamil-english, malayalam-english, and kannada-english @xcite . the objective of the task is to identify abusive content in a social media text. the shared task presents a new gold standard corpus for abusive language identification of code-mixed text in three dravidian languages: tamil-english (chakravarthi et al., 2020b), malayalam-english (chakravarthi et al., 2020a), and kannada-english @xcite . (dowlagar and mamidi, 2021) built a transformer-based transliteration and class balancing loss model to identify abusive content from code-mixed dravidian languages. tif-dnn, a transformer-based interpretation and feature extraction model to identify abusive content in hindi-english code-mixed text, is built by @xcite 3 data and methods ## results in the competition, teams were ranked based upon macro averaged precision, macro averaged recall and macro averaged f1-score across all the classes. our suggested model came in 9 th place for abusive category recognition on a code-mixed tamil-english data set among the participating teams. ## conclusion and future enhancement our work presented a model proposed by team ii-itdwd for detecting abusive categories in tamil-english code mixed text as part of the shared task dravidianlangtech@acl 2022. our proposed model came in 9 th place among the participating teams, with a significant accuracy value of 62%. in the proposed model, we employed the transformerbased indicbert, trained on indic languages, to