United States

Many social media platforms have users who have normalized the abuse of women online, creating a need for systems that automatically detect such activity. For low-resource, regional languages like Tamil, which has informal writing styles, spelling variations, dialectal differences, and culturally specific expressions, it becomes a challenge to correctly detect abusive comments. In this work, a transformer-based approach for binary classification of Tamil comments into abusive and non-abusive categories is done using the DravidianLangTech dataset. The proposed system fine-tunes MuRIL(a multilingual transformer pretrained for Indian languages), enabling effective contextual representation with minimal preprocessing. To improve the transparency of the system, a post-hoc Explainable AI component is incorporated. A perturbation-based method using log-odds differences identifies words that significantly influence the predictions. Experimental findings indicate that the model reaches a validation accuracy exceeding 81% while also exhibiting a strong macro-F1 score. This research shows that utilizing contextual multilingual representations alongside simple interpretability methods offers a viable and effective approach for detecting abusive text in Tamil. The implementation of our system is publicly available at https://github.com/mirud5173/Abusive-Tamil-Comment-Detection-using-Transformer-Models

ACL 2026

DLRG@DravidianLangTech 2026: Explainable Transformer-Based Detection of Abusive Tamil Text Targeting Women on Social Media

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

workshop paper

This paper presents an overview of the Multi-Level Political Meme Classification shared task conducted at DravidianLangTech–ACL 2026. The task introduces a hierarchical two-level classification framework for Tamil and Malayalam political memes: Level 1 focuses on stance detection (Support/Praise vs. Troll/Oppose), while Level 2 identifies the political target (individual or party), conditioned on the predicted stance. The dataset was curated from social media platforms and manually annotated with strong inter-annotator agreement. A total of 64 teams registered and 19 teams submitted their results using diverse multimodal approaches combining transformer-based text encoders, vision models, OCR pipelines, and hierarchical architectures. Results show that stance detection achieves high macro-F1 scores across both languages, whereas target identification remains more challenging, particularly in Malayalam. The findings highlight the importance of multimodal fusion, hierarchical reasoning, and robustness to OCR noise and class imbalance in political meme analysis.

Overview of the Shared Task on Multilevel Political Meme Classification in Tamil and Malayalam

peech recognition for Tamil dialects is an important and challenging task. Tamil is spoken in many different ways across regions, and this makes automatic speech recognition harder. Small changes in pronunciation, words, and speaking style can confuse a model. This system focuses on recognizing Tamil dialect speech using Whisper Tamil Large v2. The model was used without fine tuning and was trained on 579 speech samples. The main goal was to understand how well a large pre trained speech model performs in a low resource setting. Instead of modifying the model, this system directly evaluated its ability to handle dialect speech. The system was tested using Word Error Rate as the evaluation metric. It achieved a Word Error Rate of 0.71 on the test data. The results show that the model can capture basic speech patterns, but dialect differences and limited training data strongly affect accuracy. This study helps show the current limitations of large speech models for Tamil dialect recognition. It also provides a clear baseline for future work. With more data and proper fine tuning, the performance can be improved significantly.

TamilVoiceLab@DravidianLangTech 2026: Investigating Whisper Tamil Large-v2 for Dialectal Tamil Speech Recognition

This paper presents an overview of the second shared task on Abusive Tamil Text Targeting Women on Social Media as a binary classification problem (abusive vs. non-abusive). We release a dataset of Tamil YouTube comments and evaluate submissions using macro-F1 to encourage balanced performance in a noisy, low-resource setting. There are 89 teams registered for this task and 24 teams submitted the results. The approaches used by the teams includes transformer fine-tuning, heterogeneous ensembles, classical baselines, and large language models using prompting and LoRA. Results show that the best-performing system scored 0.8297 macro-F1 and many submissions are around 0.79-0.81. Across submissions, transformer fine-tuning with domain-aligned encoders is consistently strong, while additional gains are frequently associated with Tamil-aware normalization and macro-F1-oriented calibration such as class-weighted learning and validation-based threshold tuning. Overall, the findings highlights the importance of language-aware preprocessing and careful decision calibration for reliable moderation of women-targeted abusive Tamil social media text.\\ Disclaimer: This paper (including figures and examples) may contain offensive or harmful language, including abusive content targeting women. All such text is presented solely for research and educational purposes and it does not reflect the author's views. Reader discretion is advised.

From Comments to Harm: A Findings Report on Abusive Tamil Text Targeting Women on Social Media Shared Task

Generating contextually coherent multi-turn dialogue in Telugu requires resolving three deeply interacting constraints absent from generic LLM prompting: morphologically encoded social hierarchy (honorific verb conjugations), strict SOV agglutinative syntax, and culturally governed emotional logic formalised in Natyashastra rasa theory (Bharata Muni, 1951). We introduce LIMP (Linguistically-Informed Multi-Strategy Prompting), an inference-time, training-free framework that injects expert linguistic and cultural knowledge into prompt structure, requiring no fine-tuning or labelled data. We empirically evaluate two strategies on 10,000 stratified evaluation instances from the IndicDialogue Telugu corpus (Arnob et al., 2024): LIMP-RAW, a dense constraint prompt, and LIMP-COT, a six-stage analytical scaffold grounded in rasa theory and Telugu morphological grammar. Our primary finding is that LIMP-COT achieves approximately 2× higher morphosyntactic surface fidelity than LIMP-RAW on GEMMA-3-1B-IT (Gemma Team, Google DeepMind, 2025) (1B parameters): Jaccard = 0.0436 vs. 0.0211, Dice = 0.0792 vs. 0.0411 (p < 0.001, Cohen’s d = 0.57), demonstrating that sequential analytical commitment to linguistic constraints produces more form-faithful Telugu than holistic constraint injection. Concurrently, LIMP-RAW achieves near-ceiling semantic fidelity (BERTSCORE F1 = 0.9709), exceeding both LIMP-COT (0.9637) and SARVAM-1 (Sarvam AI, 2024) (2B, Indic-pretrained; 0.9680) on this dimension. This semantic–lexical dissociation—no single configuration dominates across both metric classes—is itself a substantive finding: in agglutinative Telugu, semantic paraphrase fidelity and morphosyntactic surface fidelity are orthogonal evaluation dimensions. On lexical metrics specifically, LIMP-COT with a 1B general-purpose model surpasses SARVAM-1 under matched prompting (Jaccard = 0.0436 vs. 0.0052), suggesting that structured linguistic scaffolding is a stronger lever than parametric scale for form-faithful generation.

LIMP: Linguistically-Informed Multi-Strategy Prompting for Telugu Multi-Turn Dialogue Generation

In this digital era, the use of social media and online content in regional languages like Tamil is growing exponentially. Consequently, the volume of cyber abuse and gender based hate content is also on the rise. The presence of abusive language in regional languages such as Tamil poses a huge challenge. Tamil language has a highly inflected structure, and contains a large number of informal and colloquial words, dialects, and implicit and context dependent words. Therefore, there is a strong need for robust machine learning models to back up the efforts for online safety. We experimented various fine-tuned transformer-based pretrained language models which includes mBERT, MuRIL, XLM-RoBERTa, IndicBERT and L3Cube Tamil-BERT for the task of abusive content prediction. In this work, we investigate the effectiveness of transformer based models in abusive content detection in Tamil. Our comparative study shows that the transformer-based models are very effective in identifying the implicit, contextual and subtle language abusiveness present in Tamil. Experimental results demonstrate that Tamil-BERT (L3Cube) achieves a state-of-the-art accuracy of 80.72%. Its superior performance is attributed to exclusive training on Tamil corpora, providing the morphological precision necessary to navigate the language's structural complexities. The source code and trained models are available online.

DPR@DravidianLangTech 2026: Transformer-Based Abusive Content Detection for Tamil Text Targeting Women on Social Media

This paper presents a study of speech-to-speech translation for low-resource Dravidian languages, focusing on Tamil, Telugu, and Kannada. We investigate the efficacy of the Cascaded Modular system with the End-to-end system in both zero-shot and fine-tuned settings. The Cascaded Modular approach combines an ASR Module (Whisper-based ASR for English speech; IndicConformer for Dravidian speech), a Text-to-Text translation module (IndicTrans2), and a Speech synthesis module (Indic Parler-TTS), whereas SeamlessM4T is used as the End-to-end system. For parameter-efficient Low-Rank Adaptation (LoRA) fine-tuning to adapt the translation component to a domain-specific dataset, we use FLEURS and Mann-ki-Baat (a subset of BhasaAnuvaad dataset). Cascaded Modular systems achieve BLEU scores ranging from 3.17 to 19.18 in the zero-shot setting and 5.08 to 19.18 after fine-tuning, whereas the End-to-end model ranges from 3.02 to 15.72 in zero-shot settings across languages and 4.11 to 16.84 after fine-tuning. The results show that Cascaded Modular systems consistently outperform the End-to-end model across both setups. Note that parameter-efficient fine-tuning yields significant improvements in translation quality and speech generation performance for low-resource Dravidian speech translation.

Cascaded Modular or End-to-End? : An Investigation on Speech-to-Speech Translation Task for Dravidian Languages

Among various forms of online content, hope speech plays a crucial role in promoting positivity, encouragement, and social harmony, particularly in coe-mixed and culturally diverse communities. To address the challenges of building applications for Hope Speech Detection in Code- Mixed Tulu Language shared task organized by DravidianLangTech@ACL 2026, in this paper, we team - MUCS, describe the proposed methodology submitted for the shared task. As there are no pretrained models for Tulu, we explored multiple features - word n-grams (n = 1, 3), character n-grams (n = 1, 3), syllable n-grams (n = 1, 3) and sub-word features to train ensemble of classical Machine Learning (ML) models: i) Multinomial Naive Bayes and LR classifiers and ii) kNN and DT classifiers, both with soft-voting. Experimental results demonstrate that feature integration effectively captures lexical, sub-lexical, and phonological cues in noisy code-mixed text. The system achieves competitive performance on both development and test datasets, highlighting the effectiveness of feature-based approaches for low-resource hope speech detection in code-mixed Tulu.

MUCS@Dravidianlangtech@ACL2026: Hope Speech Detection in Code-Mixed Tulu Language Using Multiple Features

Dialectal variation poses a significant challenge to Automatic Speech Recognition (ASR), particularly for low resource morphologically rich languages such as Tamil. Although widely spoken in India, Sri Lanka, and the global diaspora, Tamil exhibits substantial phonetic, lexical, and prosodic variation across dialects, complicating both dialect classification and speech recognition. In this work, we address these tasks within a unified framework. We evaluate state-of-the-art models for dialect classification, including Whisper, CLDNN, wav2vec, and wavLM, and for ASR, Whisper and a zero-shot Conformer. Among them, Whisper achieves the best performance, obtaining a macro F1-score of 0.46 for dialect classification and a word error rate of 0.57 for ASR. These results highlight the strong generalization capability of transformer-based foundation models across dialects and languages. The code is publicly available in github for research purpose.

IIITK_SpeechScape@DravidianLangTech 2026: Dialect based speech recognition and classification using Speech Foundation Models and Deep Learning Techniques

Prompt recovery in large language models (LLMs) is the task of inferring the communicative intent and stylistic framing of the original instruction from model-generated output. This task is especially challenging for low-resource Dravidian languages such as Telugu, where agglutinative morphology, register variation, and scarce annotated data complicate stylistic modelling. In this paper, we present our system for the Shared Task on Prompt Recovery for LLM in Telugu at DravidianLangTech @ ACL 2026, which aims to classifiy Telugu transcript excerpts into nine communicative style categories: Formal, Informal, Optimistic, Pessimistic, Humorous, Serious, Inspiring, Authoritative, and Persuasive. We have implemented a transformer-based approach using ai4bharat/IndicBERTv2-MLM-only, MuRIL-base and Telugu-BERT for Telugu communicative style classification. Our system fine-tunes the pretrained Indic language training samples to capture stylistic patterns in Telugu transcripts. The training setup incorporates label smoothing, the AdamW optimizer with linear warmup and decay scheduling, and early stopping based on macro-averaged F1 to ensure stable convergence. Our approach achieved a macro F1 score of 0.2993 on the evaluation set, demonstrating the effectiveness of Indic-focused pretrained models for stylistic analysis in low-resource language settings.

Cuet Yet Another Baseline@DravidianLangTech 2026: Shared Task on Prompt Recovery for LLM in Telugu

Detecting abusive language in Tamil social media is a genuinely difficult problem. The language is morphologically rich, speakers routinely mix Tamil with English, and informal romanised Tamil is common enough to confuse models trained primarily on formal text. This work presents a system for binary classification of Tamil comments into Abusive and Non-Abusive categories, submitted to the DravidianLangTech@ACL 2026 shared task. MuRIL, a BERT-based encoder pre-trained on 17 Indian languages and their transliterated equivalents, is fine-tuned, and it is shown that this Indian-language-specific pre-training provides a meaningful advantage over generic multilingual baselines. The system achieves a macro-averaged F1 of 0.83 on the validation set, compared to 0.79 for XLM-RoBERTa and 0.77 for mBERT under identical training conditions, establishing a strong transformer-based baseline for abusive language detection in code-mixed Tamil.

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