O ptimize_ P rime@ D ravidian L ang T ech- ACL 2022: Abusive Comment Detection in T amil (2022)

Abstract: This paper tries to address the problem of abusive comment detection in low-resource indic languages. Abusive comments are statements that are offensive to a person or a group of people. These comments are targeted toward individuals belonging to specific ethnicities, genders, caste, race, sexuality, etc. Abusive Comment Detection is a significant problem, especially with the recent rise in social media users. This paper presents the approach used by our team — Optimize_Prime, in the ACL 2022 shared task “Abusive Comment Detection in Tamil.” This task detects and classifies YouTube comments in Tamil and Tamil-English Codemixed format into multiple categories. We have used three methods to optimize our results: Ensemble models, Recurrent Neural Networks, and Transformers. In the Tamil data, MuRIL and XLM-RoBERTA were our best performing models with a macro-averaged f1 score of 0.43. Furthermore, for the Code-mixed data, MuRIL and M-BERT provided sublime results, with a macro-averaged f1 score of 0.45.

## introduction the rise in social media platforms like facebook and twitter has led to the exchange of massive amounts of information on the internet. with the increase in the number of users and platforms, problems like hate speech and cyberbullying have also increased @xcite . abusive comments are comments that are offensive towards a particular individual or a group of individuals. online abuse has led to problems like lowered self-esteem, depression, harassment, and even suicide in some severe cases. hence detecting and dealing with such comments is of utmost importance. classifying detected comments helps determine the severity of the comment and will also help the authorities * first author, equal contribution † second author, equal contribution take appropriate action against the individual. our task is to detect and classify abusive comments written in tamil. abusive comment detection is a text classification problem. text classification is a technique that extracts features from text and assigns a set of predefined categories(classes) to it. traditionally, text classification was done using linear classifiers on the sentence embeddings of text. this was followed by recurrent neural networks like lstms, which gave promising results. after the paper @xcite , transformers were introduced in the field of natural language processing. they have an attention layer that provides context to words in the text. the introduction of the transformer architecture has led to the development of many other variations of the transformer like bert @xcite , xlm-roberta @xcite , muril @xcite , etc. in this paper, we use different transformer-based models for abusive comment detection in tamil. we have also used rnn models like lstms, a newer model ulmfit and a type of ensemble model. we compared the results obtained from all three approaches to determine the optimum model for this task. ## related work tamil is a low-resource language, so finding properly annotated data is challenging. in order to encourage research in tamil, datasets have been created by @xcite . the paper, @xcite tries an rnn based approach for detecting offensive language in tweets. arora (2020) developed a model for detecting hate speech in tamil-english codemixed social media comments using a pre-trained version of ulm-fit. after the introduction of transformers in @xcite , the use of transformers for nlp tasks increased. the release of bert @xcite paved the way for many more variations of transformers. in the paper, @xcite the results for the hasoc in indo-european languages were showcased where they used multilingual bert and monolingual bert. some work has been done by @xcite in english, malayalam, and tamil, aiming to detect hope speech which is also a text classification task. they fine-tuned xlm-roberta @xcite for hope speech detection. the development of models like muril @xcite , xlm-roberta @xcite , indic bert @xcite , and m-bert @xcite has encouraged research in various low resource as well as high resource languages. ## dataset description the shared task on abusive comment detection in tamil-acl 2022 aims to detect and reduce abusive comments on social media. the main objective of the shared task is to design systems to detect and classify instances of hate speech in tamil and tamil-english codemixed youtube comments. the abusive comment detection dataset priyadharshini et al. (2022) consists of tamil and tamil-english comments collected from the youtube comments section. the dataset consists of a comment and its corresponding label belonging to the nine labels in the dataset: misandry, counterspeech, misogyny , xenophobia, hope-speech, homophobia, transphobic, not-tamil, and noneof-the-above. ## methodology to classify youtube comments, we used three different approaches: ensemble models, recurrent neural networks, and transformers. foot_0 4.1 data pre-processing ## results the results obtained by all the models can be viewed in ## conclusion this paper aims to detect and classify abusive comments. we tried three approaches for abusive comment detection in tamil and tamil-english code-mixed data: ensemble models, recurrent neural networks, and transformer-based models. for the tamil data, muril and xlm-roberta provided the best results with a macro-averaged f1 score of 0.43. classes like homophobia and misandry were predicted with higher accuracy than others like transphobic and counter-speech. sentences that are not abusive are also classified well. for the codemixed data, muril and m-bert outperformed all other models with a macroaveraged f1 score of 0.45. classes like xenophobia, misandry, and transphobic were predicted with higher accuracy than others for the codemixed data. sentences that are not abusive are also classified well. in the future, various techniques can be tried to improve the performance of the models. in order to boost the performance, genetic algorithm-based ensembling methods could be used.