Work

TriVector@DravidianLangTech 2026: Abusive Tamil Text Detection on Social Media Using Lexicon-Augmented Transformers

Year: 2026

Type: conference-paper

Abstract: Abusive comment detection in low-resource languages poses significant challenges, particularly when targeting gender-based abuse on social media platforms.This work presents our system for 'Abusive Tamil text targeting women on social media' at Dra-vidianLangTech@ACL 2026.We introduce nine handcrafted lexicon features integrated with pretrained multilingual transformer embeddings and evaluate their effectiveness in classifying Tamil online comments as abusive or non-abusive.To better understand their impact, we compare model performance with and without these lexical attributes across multiple transformer architectures.Our best-performing model, XLM-RoBERTa-Large, achieved a macro F1-score of 81.71%, securing 15th rank in the competition.The findings indicate that larger multilingual models generalize more effectively to unseen data compared to smaller domain-specific models, while the addition of lexical features yields only mild gains.• We compare four pretrained models of varying scale and domain backgrounds across multiple system configurations.

Source: Workshop on Speech, Vision, and Language Technologies for Dravidian Languages (DravidianLangTech)

DOI: 10.18653/v1/2026.dravidianlangtech-1.67

Authors: Oarisa Rebayet, Tahmima Hoque Eid, Fawzia Tabassum, Hasan Murad

Institution: Chittagong University of Engineering & Technology

Language: English

Topic: Hate Speech and Cyberbullying Detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Gender equality

Open Access status: hybrid

OpenAlex ID: W7166881072