Sandalphon@ D ravidian L ang T ech- EACL 2024: Hate and Offensive Language Detection in T elugu Code-mixed Text using Transliteration-Augmentation (2024)

Abstract: Hate and offensive language in online platforms pose significant challenges, necessitating automatic detection methods. Particularly in the case of codemixed text, which is very common in social media, the complexity of this problem increases due to the cultural nuances of different languages. DravidianLangTech-EACL2024 organized a shared task on detecting hate and offensive language for Telugu. To complete this task, this study investigates the effectiveness of transliteration-augmented datasets for Telugu code-mixed text. In this work, we compare the performance of various machine learning (ML), deep learning (DL), and transformer-based models on both original and augmented datasets. Experimental findings demonstrate the superiority of transformer models, particularly Telugu-BERT, achieving the highest f_1 -score of 0.77 on the augmented dataset, ranking the 1^{st} position in the leaderboard. The study highlights the potential of transliteration-augmented datasets in improving model performance and suggests further exploration of diverse transliteration options to address real-world scenarios.

## introduction in recent years, the growing problem of offensive language in user-generated content on online platforms and its harmful impacts has become a primary concern. the vast amount of daily usergenerated content poses a significant challenge in the fight against offensive language. consequently, automated methods are necessary for handling this task. understanding code-mixed data is difficult due to several factors. first, it requires a deep understanding of the different linguistic levels involved. second, the complex structure of code-mixed language makes it challenging to analyze. finally, there needs to be more training data available for code-mixed languages, which hinders the development of effective classification systems. these challenges can lead to inaccurate classifications, mainly when using systems trained only on monolingual data. when a system trained on a single language encounters code-mixed data, it may be unable to accurately identify the different languages or understand the complex grammar rules involved. these challenges were addressed in the studies of @xcite , which introduced a shared task for detecting hate and offensive language in telugu. this paper contributes to the ongoing research efforts explored at the dravidianlangtech-eacl2024 @xcite . the primary contributions of this work are illustrated in the following: ## related work social media can often spread negativity and hurtful content. it is important to recognize such content because it can offend individuals and groups based on race, gender, or religion @xcite . to maintain the integrity of the social media ecosystem, researchers and stakeholders must focus on creating computational models capable of swiftly detecting and categorizing offensive content @xcite . hence, to address this growing concern on social media platforms, earlier research utilized diverse machine learning algorithms such as linear regression, support vector machine, and naive bayes for the automated identification of hate speech @xcite . the performance of these models falls short as they need help to capture the semantic and contextual information present in textual data. utilizing a publicly available dataset of tweets, @xcite suggests a methodology for automating tweets into three classes: hate, offensive, and neither. their approach utilizes bilstm models with blank embedding and pre-trained glove embeddings to identify offensive language and hate speech. in recent years, pre-trained models have demonstrated remarkable accuracy in classifying code-mixed texts across various languages @xcite . the current focus of research in text analysis is within the transliteration domain, which holds the potential for achieving superior results @xcite . ## methodology figure 2 illustrates the overall process, including all employed models for the task. various techniques are used to extract textual features for training the respective ml and dl models. this work also utilized transformer-based techniques, such as m-bert, xlm-r, indic-bert, and telugu-bert. ## results and analysis this section analyzes the effectiveness of different models for detecting hate speech and offensive language in telugu code-mixed texts. the performance of the models is evaluated based on macro-averaged f 1 -score. the evaluation result report is shown in telugu-transliterated texts. ## conclusion this paper explored various ml, dl, and transformer-based approaches for hate and offensive language detection in telugu codemixed text and demonstrated the effectiveness of transliteration-augmented datasets. in most cases, transformer models outperformed ml and dl methods. telugu-bert, trained explicitly on telugu text, achieved an impressive f 1 score of 0.77. investigating further augmentation methods and their combinations presents an exciting future direction to enhance the dataset and improve performance.