DE - ABUSE @ T amil NLP - ACL 2022: Transliteration as Data Augmentation for Abuse Detection in T amil (2022)

Abstract: With the rise of social media and internet, thereis a necessity to provide an inclusive space andprevent the abusive topics against any gender,race or community. This paper describes thesystem submitted to the ACL-2022 shared taskon fine-grained abuse detection in Tamil. In ourapproach we transliterated code-mixed datasetas an augmentation technique to increase thesize of the data. Using this method we wereable to rank 3rd on the task with a 0.290 macroaverage F1 score and a 0.590 weighted F1score

## introduction internet is a global computer network that provides a variety of information and facilitates communication between users from any part of the world. the world population is 7.9 billion as of january 2022 of which around 5.2 billion are live internet users 1 . in recent times, people have become more communicative and inclusive. people want to share their views on a common platform, where social media comes into the picture @xcite @xcite . people can post their opinions which are productive and efficient for their society but at times people also post their opinions which could be abusive to others. there are many social media platforms like youtube, facebook, instagram, twitter and many more @xcite where the users are given the liberty to put forward their opinion. on an average as per statistics around 250 m tweets are posted, 2 million blogs are written on various websites and 80 b mails are sent per day. social media platforms could be both a boon and bane. comments that humiliates or denigrates an individual or a group based on various characteristics such as colour, ethnicity, sexual orientation, 1 https://www.internetlivestats.com/ nationality, race and religion are called abusive comments @xcite . abuse caused via social media can cause many negative impacts in users' lives. this will affect the mental state of the specific individual terribly causing depression and sleeplessness @xcite @xcite . some of these comments also can create a controversy over the social media on a specific individual or a group of people. this shows the need for restricting these kind of abusive comments from being posted in the social media. once abusive comments have been posted onto the social media it should be flagged and immediately removed. this world is a diverse one which comprises of different kinds of people from different origin. but when it comes to the comments of people the language plays a very important role @xcite . though most of the people use english as their language to show their opinion some of them also use other languages instead of english. for example in a diverse nation like india where people are not restricted to communicate in english, people comment in different languages like tamil, telugu, kannada, malayalam, hindi, marathi and many others. tamil is one of the oldest and longest surviving language in this world @xcite . it is an old dravidian language mostly spoken by people of south indian origin with a history of over 3000 years foot_0 that has lot of dialects. therefore it is very tough to classify posts which have abusive comments in tamil language. lately, after the advent of machine learning, researches are carried over onto this area for classifying the abusive comments. in our work we have used transformer @xcite models for the given task of classifying the abusive comments. the rest of the paper is structured as follows section 2 describes the related works which are carried over in this field. the section 3 describes the methodology used in the system. the 2nd section describes the results we obtained in our research experiments.we discuss our results on section 4. finally in the 5th section we conclude this research paper followed by the references section. ## methodology in this section, we describe the methodology based on which our system is designed, including the data preparation phase, modelling phase and model evaluation phase. ## results muril and other machine learning models were trained on the training set and was validated on the dev set. for this competition, submission, macro f1-score was considered as the metric of evaluation by the organisers. by this muril trained on both tamil and transliterated dataset combined together had a very high macro f1-score of 0.49 and weighted f1-score of 0.76 on the validation dataset and a macro f1-score of 0.290 on test dataset and weighted f1-score of 0.590. with this result we secured the 3 rd rank in the task. the ## conclusion in this paper, we conclude that with a relatively smaller-size dataset, we can use transliteration as an efficient data augmentation technique to increase the volume of data available which played a very important role for getting a better f1-score is evident from the results