United States

Low-resource languages pose significant challenges for speech technology due to linguistic variation and limited annotated resources. One such language is Tamil, which is a morphologically rich language with significant dialectal variations, which makes Automatic Speech Recognition (ASR) and dialect classification a challenging task. In this article, we introduce a shared-task system for handling Speech Processing in Tamil Language covering both ASR and Dialect classification. We use the Whisper Large-v3 multilingual model in a zero-shot setting without task-specific fine-tuning. For dialect classification, we employ a pre-trained Wav2Vec2 model to extract acoustic features and mean and standard deviation pooling to create utterance-level representations, with an XGBoost model trained for four-way prediction of dialects. Experiments on 579 Tamil speech samples resulted in a word error rate (WER) of 0.61, highlighting the difficulty of the dialectal ASR problem in low- resource setting. The dialect classification system obtained an accuracy of 0.49 and a macro F1 score of 0.41, and there was a certain amount of confusion between the dialect classes. The proposed system is purely based on the standard pretrained models without adaptation, but has produced a benchmark that can be replicated in the multilingual speech representation evaluation of Tamil low-resource scenarios. The results also indicate the need for additional strategies to improve the robustness of the model and stronger baseline models and improved methods for embedding-based dialect classification for future research.

ACL 2026

AITamilDialect@DravidianLangTech 2026: Zero-Shot Whisper and Wav2Vec2 Embedding-Based Tamil Speech Recognition and Dialect Classification.

multilingual speech models

low-resource speech processing

whisper large-v3

tamil asr

dialect classification

wav2vec2

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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workshop paper

It is difficult to detect abusive language, particularly in social networks for low-resource languages like Tamil. Spelling errors, informal expressions and code-mixing make it even more challenging to read text from social media. The current work proposes a multilingual transformer-based approach to detect abusive content in Tamil text. A pretrained XLM-RoBERTa model is used to learn contextual and semantic representations from the input text. This is a general pipeline comprising preprocessing, tokenization, and binary classification (abusive / non-abusive). Experiments are performed on Tamil social media datasets that have abusive and non-abusive data. The results reveal that multilingual transformer models achieve good performance in low-resource scenarios. The proposed model attains an F1-score of 78.64%, which shows the potential of using cross-lingual pretrained models for the detection of abusive Tamil language.

TAMILGOODBADTXT@DravidianLangTech 2026:A Multilingual Transformer-Based Approach for Abusive Language Identification in Tamil Social Media

The proliferation of misogynistic content on social media platforms is a serious problem that requires the development of automated detection systems, which is a challenging task for low-resource languages like Tamil. This study investigates the effectiveness of multilingual transformer models for identifying abusive Tamil text targeting women in social media. Results indicate that such models achieve strong baseline performance on this task. Furthermore, an ensemble of two best performing models was found to improve the classification performance further. The results also highlighted the significance of domain-specific pre-training for improving classifier performance. The best performing ensemble model achieved a weighted F1 score of 0.83 on the test set, placing our approach in first position in the shared task.

NITC-HSR@DravidianLangTech 2026: Ensembling Multilingual Transformer Models for Detecting Abusive Tamil Text Targeting Women on Social Media

Political memes are widely used to express opinions, sarcasm, and ideological narratives on social media platforms. However, detecting political trolling in low-resource languages such as Tamil and Malayalam remains challenging due to limited datasets and tools. To address this problem, DravidianLangTech@ACL 2026 organized a shared task on hierarchical political meme classification.

This work explores text-only models, classical multimodal fusion, and Vision-Language Models (VLMs) for Tamil and Malayalam political meme classification. Our experiments include IndicBERTv2, XLM-RoBERTa, EfficientNet-based multimodal fusion, and Qwen-VL models. Among the submitted systems, Qwen2.5-VL-7B-Instruct with 4-bit QLoRA fine-tuning achieved competitive performance, ranking 3rd in the Malayalam track and 4th in the Tamil track based on weighted-F1 score. Additional post-evaluation experiments with Qwen3-VL-8B further improved macro-F1 performance, highlighting the effectiveness of VLMs for low-resource multilingual political meme classification.

Semantica@DravidianLangTech 2026: Vision-Language Models for Hierarchical Political Meme Classification in Tamil and Malayalam

The increasing prevalence of social media has also correlated with an increase in abusive content targeting women, particularly for regional languages such as Tamil. The automatic identification of abusive content is critical for the creation of safer online spaces. In this paper, we focus on the abusive text detection of women in the context of binary text classification. We evaluated the performance of the proposed system on the abusive text detection of women using the IndicBERT, MuRIL, and Tamil-BERT models. Additionally, we propose the use of grapheme-aware normalization for the proposed system. Grapheme-aware normalization aims to maintain the structural integrity of Tamil characters at the Unicode level. The experimental results reveal that the proposed system using the Tamil-BERT model with grapheme-aware normalization achieves the best performance among the evaluated models. The proposed system achieved the third position in the shared task.

HNK@DravidianLangTech 2026: Investigating Grapheme-Level Normalization for Abusive Tamil Text Classification

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

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

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.

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