Aanisha@ T amil NLP - ACL 2022:Abusive Detection in T amil (2022)

Abstract: In social media, there are instances where people present their opinions in strong language, resorting to abusive/toxic comments. There are instances of communal hatred, hate-speech, toxicity and bullying. And, in this age of social media, it’s very important to find means to keep check on these toxic comments, as to preserve the mental peace of people in social media. While there are tools, models to detect andpotentially filter these kind of content, developing these kinds of models for the low resource language space is an issue of research. In this paper, the task of abusive comment identification in Tamil language, is seen upon as a multi-class classification problem. There are different pre-processing as well as modelling approaches discussed in this paper. The different approaches are compared on the basis of weighted average accuracy.

## introduction with social media being accessible and popular across masses in india, there has been a surge in content in regional languages. people often create content, comment or exchange messages in monolingual or code mixed language @xcite @xcite . however,even if there is an abundance of content in indian language across social media, there is a lack of indian language datasets @xcite . hence indian languages are deemed as low resource language space, due to lack of available datasets, making working in these languages spaces, a challenging research problem @xcite . among the messages and comments exchanged on social media there are instances of monolingual comments in regional language as well as transliterated comments. monolingual comments in transliterated means to write or print (a letter or word) using the closest corresponding letters of a different alphabet or script. code-mixing is mixing of two or more language in the same utterance @xcite @xcite @xcite . in this paper, the task is identifying abusive comments in tamil language. tamil is a member of the southern branch of the dravidian languages, a group of about 26 languages indigenous to the indian subcontinent @xcite . it is also classed as a member of the tamil language family, which contains the languages of around 35 ethno-linguistic groups, including the irula and yerukula languages @xcite @xcite . the earliest old tamil documents are small inscriptions in adichanallur dating from 905 bc to 696 bc. this is a multiclass classification problem, with 6 different categories of abusive comments are present. in a multi class classification problem, an instance can belong only to one class. however present machine learning or deep learning based models cannot be directly applied to tamil language. thus several pre-processing techniques have been proposed for tamil language and models have been fine tuned to suit the task @xcite @xcite @xcite ). ## related work there has been different works done on identifying abusive comments on different languages. in @xcite performed binary and multiclass classification using a twitter corpus and studied two approaches: (a)a method which consists in extracting of word embeddings and then using a dnn classifier; (b) fine-tuning the pre-trained bert model.however it was only on english language embeddings. in @xcite detected hate speech from hindi-english code mixed conversations on twitter. the proposed architecture used neural networks, leveraging the transformer's cross-lingual embeddings and further fine tuning them for lowresource hate-speech classification in transliterated hindi text. in (andrew, 2021) as a part of shared task in acl dravidian lang tech 2021, several machine learning algorithms were compared and experimented for identifying abusive comment in various dravidian languages. ## dataset the dataset is provided by @xcite as a part of the shared task abusive comment detection in tamil. the dataset has a collection of comments in tamil language.there are 2240 native tamil script comments and 5943 transliterated tamil-english comments in the train data,classified across 7 different categories : 'hope-speech', 'homophobia', 'misandry', 'counter-speech', 'misogyny', 'xenophobia', trans-phobic' and 'none-of-the-above'. the validation data has 560 native tamil script comments and 1486 transliterated tamil-english comments.the test data has 699 native tamil language comments and 1857 transliterated tamil-english comments. the most dominant category present across all the datasets is : 'none-of-the-above' and the categories with less no of comments are 'homophobia' with 207 and 'trans-phobic' with 163 total comments. ## implementation 6.0.1 logistic regression the original training data contains 10227 comments and the test data contains 2555 comments. the data is first tokenized using the indicnlp tokenizer and feature vectors are generated by using tf-idf with unigrams and bigrams being extracted. the feature vector are fed to the logistic regression model with a newton-cg solver, to accomodate multiclass classification. there are two experiments that are run for this model.the model is trained on original dataset and the model is trained on sampled dataset. ## results ## future work the future work will be primarily to find more efficient sampling techniques for the text data, and compare the performances with further ml models. also, evaluate performances with other existing transformer models,to check how different suitable models can be fine-tuned to solve this particular task.