GJG@TamilNLP-ACL2022: Using Transformers for Abusive Comment Classification in Tamil
This paper presents transformer-based models for the “Abusive Comment Detection” shared task at the Second Workshop on Speech and Language Technologies for Dravidian Languages at ACL 2022. Our team participated in both the multi-class classification sub-tasks as a part of this shared task. The dataset for sub-task A was in Tamil text; while B was code-mixed Tamil-English text. Both the datasets contained 8 classes of abusive comments. We trained an XLM-RoBERTa and DeBERTA base model on the training splits for each sub-task. For sub-task A, the XLM-RoBERTa model achieved an accuracy of 0.66 and the DeBERTa model achieved an accuracy of 0.62. For sub-task B, both the models achieved a classification accuracy of 0.72; however, the DeBERTa model performed better in other classification metrics. Our team ranked 2nd in the code-mixed classification sub-task and 8th in Tamil-text sub-task.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationMulti-class ClassificationSimilar Papers 제목 키워드 기반
UMUTeam@TamilNLP-ACL2022: Abusive Detection in Tamil using Linguistic Features and Transformers
Social media has become a dangerous place as bullies take advantage of the anonymity the Internet provides to target and intimidate vulnerable individuals and groups. In the past few years, the research community has foc…
PositionSentenceSentence EmbeddingsAanisha@TamilNLP-ACL2022:Abusive Detection in Tamil
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 ag…
Multi-class ClassificationSSNCSE NLP@TamilNLP-ACL2022: Transformer based approach for detection of abusive comment for Tamil language
Social media platforms along with many other public forums on the Internet have shown a significant rise in the cases of abusive behavior such as Misogynism, Misandry, Homophobia, and Cyberbullying. To tackle these conce…
MUCIC@TamilNLP-ACL2022: Abusive Comment Detection in Tamil Language using 1D Conv-LSTM
Abusive language content such as hate speech, profanity, and cyberbullying etc., which is common in online platforms is creating lot of problems to the users as well as policy makers. Hence, detection of such abusive lan…
Abusive LanguageBpHigh@TamilNLP-ACL2022: Effects of Data Augmentation on Indic-Transformer based classifier for Abusive Comments Detection in Tamil
Social Media platforms have grown their reach worldwide. As an effect of this growth, many vernacular social media platforms have also emerged, focusing more on the diverse languages in the specific regions. Tamil has al…
Abusive LanguageData Augmentation