Hypers at C om MA @ ICON : Modelling Aggressive, Gender Bias and Communal Bias Identification (2021)

Abstract: Due to the exponential increasing reach of social media, it is essential to focus on its negative aspects as it can potentially divide society and incite people into violence. In this paper, we present our system description of work on the shared task ComMA@ICON, where we have to classify how aggressive the sentence is and if the sentence is gender-biased or communal biased. These three could be the primary reasons to cause significant problems in society. Our approach utilizes different pretrained models with Attention and mean pooling methods. We were able to get Rank 1 with 0.253 Instance F1 score on Bengali, Rank 2 with 0.323 Instance F1 score on multilingual set, Rank 4 with 0.129 Instance F1 score on meitei and Rank 5 with 0.336 Instance F1 score on Hindi. The source code and the pretrained models of this work can be found here.

## introduction the internet is a vast network that connects devices all over the world. due to mobile technology and affordable internet plans, users can access the internet with ease, leading to the tremendous growth of the internet, which is unprecedented. as of january 2021, there were 4.66 billion active internet users, 59.5% of the world's population. users would undoubtedly want to increase their reach virtually, and hence the interaction among the people would increase. the people these days are more vocal and, at any cost, want their voices or opinions to be reached to a multitude of people. hence, people search for a platform to share their views, and social media is an ideal place for that. this exact mindset of people has fueled the copious amounts of social media users globally. social media are the technologies that allow the creation, sharing, or exchange of information, interests, ideas, and other forms of expression. its use is an ever-increasing phenomenon of the 21st century @xcite . there are a plethora of social media platforms, each attracting people in unique ways. as of january 2021, there were 4.2 billion active social media users. considering the reach of social media, they can spread people's opinions in a few minutes @xcite . hence it will have a significant effect on society which could be both positive as well as harmful @xcite . but there are instances in which the situation would go out of hand. for example, people could differ in their opinions, and people with similar views tend to form a group to denounce the group with ideas that are not the same as theirs. during the denouncement, there is a possibility that a user could show his improper behaviour, thus making offensive @xcite , misogynistic @xcite , hateful @xcite , or any kind of statements that has the potential to create controversy @xcite . such statements may be intended towards an individual or a group and are not considered to be good or acceptable in the society. as such behaviour would influence others wrongly and instigate violence or affect the mental health, leading to unpleasant situations. hence it is necessary to flag such posts and its advisable to take them down from the social media platform and also retribute the user responsible for such posts @xcite . there could be several reasons that a post by the user is considered inappropriate. considering how important it is to regulate toxic post, in this paper we will be presenting a system to identify if the user is being aggressive on some individual or a community, or being biased regard the rest of the paper is structured as follows, section 2 describes about dataset used for the shared task. the section 3 describes the models and architectures that were used for the tasks. in section 4 we discuss about the results obtained during the study, and the last section 5 concludes the work. ## dataset the comma dataset was provided in this task @xcite . the dataset had annotations for aggression, gender bias, and communal bias identification for multi-lingual social media sentences @xcite . the dataset comprises of code-mixed sentences has 15,000 code-mixed sentences. it is divided into 12,000 sentences for development and 3,000 sentences for the test. the data is divided into four sets, namely meitei, bengali, hindi, and multi-lingual. the multi-lingual set comprises sentences of all three languages. the the samples count of classes is far from equal. hence the dataset is quite imbalanced. the dataset distribution is displayed in the ## methodology in this section, we describe the methodology of our systems, including data preprocessing and model architecture. we use mean pooled, and attention pooled pretrained models, which was shown to provide better results @xcite . we trained all the models with the batch size of 8, dropout of 0.3, linear scheduler for learning rate scheduling with 2e-5 as an initial learning rate. ## results pretrained models with different pooling methods were trained on each language set and then validated on dev sets. for the competition submissions, we submitted the model with a higher micro f1 score on the dev set. ## conclusion in this paper, we experimented with different pooling methods, namely attention pooling and mean pooling and pretrained models, to classify sentences, how aggressive they are, and whether gender-oriented or communal. from