Simon @ D ravidian L ang T ech- EACL 2021: Detecting Offensive Content in K annada Language (2021)

Abstract: This article introduces the system for the shared task of Offensive Language Identification in Dravidian Languages-EACL 2021. The world’s information technology develops at a high speed. People are used to expressing their views and opinions on social media. This leads to a lot of offensive language on social media. As people become more dependent on social media, the detection of offensive language becomes more and more necessary. This shared task is in three languages: Tamil, Malayalam, and Kannada. Our team takes part in the Kannada language task. To accomplish the task, we use the XLM-Roberta model for pre-training. But the capabilities of the XLM-Roberta model do not satisfy us in terms of statement information collection. So we made some tweaks to the output of this model. In this paper, we describe the models and experiments for accomplishing the task of the Kannada language.

## introduction the network platform builds a brand new living and cultural space and promotes communication among netizens. this leads to an exponential growth of information and speech in cyberspace. the popularity of the internet facilitates the spread of offensive remarks, and also brings some negative social effects @xcite . the people who communicate on the internet come from different countries @xcite . due to the differences in history and culture of different countries, people in different regions have great differences in their understanding of offensive speech @xcite . so it's easy for people to inadvertently make offensive remarks about other people. offensive language is not a recent phenomenon, but its impact is growing because of its rapid spread on social media. a large amount of offensive language on the internet can cause serious social problems. therefore, detection of offensive content is very necessary. this is not only conducive to purifying the network environment but also conducive to promoting the positive development of society @xcite . kannada is a dravidian language spoken mainly by karnataka residents in india's south western region (chakravarthi, 2020b). after ad 600, kannada developed from the old tamil script. tamil has the most ancient indian non-sanskritic literature of indian languages. the kappe arabhatta record from ad 700 is the oldest extant record of kannada poetry in tripadi meter. the data set used in the experiment is provided by the competition organization @xcite @xcite . the data was collected from various social media platforms. in the following content, we introduce recent relevant work, the models we use, our experimental processes, and results. ## related work online social media is now one of the main channels of communication. but the growth of offensive content is troubling users and the companies that run social media. the need to identify offensive content has increased dramatically. the purpose of the identification of offensive content is to reduce offensive content in social media and thus improve users' communication experience on social media. some scholars have done a lot of work on the classification of offensive language. offensive language attacks people for a variety of reasons. people can be targeted with offensive language because of their gender, skin color, or nationality @xcite @xcite . in the field of detecting offen- ## experiment and results we combined the training set and validation set provided by the contest organizer, and then used stratified k-fold cross validation to get the new training set and validation set. cross-validation divides the data set multiple times. the common method of randomly dividing the data set will make the result have a contingency. cross-validation reduces the chance that comes with a random partition. the model can complete the training of various types of data so as to improve the generalization ability of the model. the scale relationship of the label in each layer of data is saved. we used xlm-roberta-base 1 as our preprocessing model. in the experiment, the hyper-parameters we used are shown in ## conclusion our ranking in this task is not ideal, and our test scores are not satisfactory. we think there are the following reasons: first, our team did not set the hyperparameters reasonably in the experiment, for example, the value setting of the epoch was small. second, the classification task has a large number of categories, and the nature of the tags is very similar. that makes the task more difficult. third, the data set is imbalanced. in the future, we will improve our model to accommodate the data imbalance.