United States

This paper describes Team CHMOD_777’s system for the DravidianLangTech@ACL 2026 shared task on Tamil dialect speech recognition and classification. The task comprises two subtasks: classifying Tamil speech into four regional dialects (Northern, Southern, Western, Central) and transcribing dialectal Tamil speech to text. For dialect classification, we fine-tune MMS-1b-all with Focal Loss and weighted sampling, achieving 83.04% Macro F1 on the development set (5th out of 11 teams on the test set). For speech recognition, we fine-tune a Tamil-specific Whisper model (763M parameters), achieving 53.72% WER on the development set and 49.75% on the official test set, ranking 1st out of 13 teams. Our key finding is that domain-specific pre-training significantly outperforms larger general-purpose models: Tamil Whisper (763M) beats Whisperlarge-v3 (1.5B) by 8 WER points despite having half the parameter

ACL 2026

CHMOD_777@DravidianLangTech 2026: Tamil-Adapted Whisper and MMS for Dialect Speech Recognition and Classification

low-resource speech modeling

whisper fine-tuning

speech dialect classification

tamil dialect speech recognition

automatic speech recognition

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

Hate speech detection in low-resource, code-mixed languages is a challenging task as people often switch between scripts and languages in a single post. Code-Mixed scripts can take the form of explicit slurs, subtle insults, or fragmented abuse, and is often hidden by spelling variants and Romanized script. These datasets are also subjected to class imbalance with hate speech being a minority class of interest. To mitigate the imbalance, targeted data augmentation of minority class samples can help learn better representations to aid hate speech detection despite the naturally expected imbalance. We propose FLAICOL, a flip-point method which identifies the minimal embedding perturbation that moves an input across the decision boundary, map it back to discrete text, and retrain on those focused examples. Empirical results show that these interpretable augmentations strengthen Transformer classifiers on low-resource, code-mixed low resource hate datasets (Experiments were conducted on the Tamil-English, Malayalam-English, and Kannada-English splits in the Dravidian CodeMix Benchmark).

FLAICOL: Flip-Point-Led Augmentation for Imbalanced Code-Mixed Offensive Language Detection

Tokenization is fundamental to neural language modeling, yet for Tamil it remains largely adapted from general-purpose multilingual models without systematic consideration of its rich agglutinative morphology. We introduce TamilMorph, a large-scale dataset of over 500,000 morphologically segmented Tamil word forms. With this new morphological resource, we develop TamilTok, a morphology-aware tokenization framework that incorporates explicit morpheme structure into tokenizer training. We benchmark Tamil tokenization quality across multiple segmentation algorithms and vocabulary configurations and find that our approach improves both morphological alignment and downstream performance compared to previous approaches. Together, these contributions establish both a foundational linguistic resource and empirically grounded guidance for tokenizer design in morphologically rich languages, contributing to more inclusive language technologies.

TamilTok: Morphologically-Informed Tokenization for Tamil

Recovering writing style prompts in low resource languages has been daunting due to diverse morphology, culturally cognizant language patterns and deficient annotated resources. As previous works have predominantly focused on binary sentiment or single attribute transfer, extensive multi-class style classification in under-resourced languages like Telegu has been vastly underexplored. In this study, we have addressed this chasm through the Telugu Prompt-Style Recovery Shared Task at DravidianLangTech@ACL 2026 (Premjith et al., 2026), framing prompt reconstruction as a nine-class classification problem with Formal, Informal, Optimistic, Pessimistic, Humorous, Serious, Inspiring, Authoritative and Persuasive as prompt styles. We have evaluated three input configurations—Change Style, Original Transcripts and Merged input style—while training three transformer based models-MuRIL, XLM-RoBERTa and IndicBERT v2 under identical conditions. Our most promising model, IndicBERT v2 with partial layer freezing and weighted cross-entropy loss, has obtained a macro-F1 of 0.2987 and accuracy of 0.299. The Change Style configuration has significantly outperformed Original and Merged inputs, indicating that explicit style changes have made tonal and meaning cues more distinctive. These results have showcased the importance of language-specific pretraining and careful input design for style-sensitive NLP in low-resource settings, ultimately securing 1st rank on the shared task.

ERROR_500@DravidianLangTech2026: Automatic Prompt Style Classification in Telugu Using Transformer-Based Language Models

Mayangoli errors are context-sensitive errors in Tamil that arise from confusion among phonetically similar graphemes (e.g., ல/ள/ழ, ர/ற, ந/ன/ண). These errors are challenging for conventional spell checkers because both incorrect and correct forms are valid dictionary words, making dictionary lookup insufficient and requiring contextual modelling. We present TamilMayangoliSpell, a reproducible framework for Mayangoli error correction that combines (i) Tamil-specific preprocessing for sentence segmentation and normalisation, (ii) linguistically grounded error induction for generating training data constrained by dictionary validity, and (iii) fine-tuning of multilingual sequence-to-sequence models. Using 30,000 sentence pairs derived from TamilCorp, a massive multi-genre Tamil corpus and split 80/10/10 into train/validation/test, we fine-tune mBART, mT5, and NLLB under a small hyperparameter grid using greedy decoding with a maximum sequence length of 128. mT5 achieves the best performance (BLEU 99.28; Exact Match Accuracy 93.50%) and remains strong in a cross-genre evaluation on short stories. The preprocessing scripts, generated parallel datasets, and trained models are publicly available in a GitHub repository.

TamilMayangoliSpell: An Open-Source Neural Framework for Context-Sensitive Mayangoli Error Correction in Tamil

In Dravidian languages, political memes have progressively influenced public opinion and political discourse, influencing digital conversations and public narratives. Our paper has proposed a multi-level multimodal framework for political meme classification in Tamil and Malayalam as part of the Multi Level Political Meme Classification- DravidianLangTech@ACL 2026 shared task. The task has involved two levels: Level 1 has identified whether a meme expresses Troll/Oppose or Support/Praise, while Level 2 has determined the specific target category (Individual, Party, or Intersection). We have evaluated unimodal and multimodal architectures to analyze the impact of textual and visual representation. Experimental results have highlighted the importance of a multimodal approach over unimodal approaches. This work has confirmed the effectiveness of combining image and text features in meme understanding. Among the evaluated models, the mBERT+ViT architecture has achieved the best overall performance across both languages and classification levels. Our approach has enabled us to secure 6th rank, achieving an F1 score of 
for the Tamil task and 2nd rank, achieving an F1 score of 
for the Malayalam task. Despite moderate results, challenges have remained mainly due to the dataset size, class imbalance, and noisy text extraction from images

CUET-2567@DravidianLangTech-ACL 2026: Multimodal Stance and Target Identification in Dravidian Political Memes

Political sentiment analysis in Tamil social media is challenging due to informal language, sarcasm, emoji-driven sentiment inversion, and severe class imbalance. This paper presents TamilEcho, our system submitted to the Shared Task on Political Multiclass Sentiment Analysis of Tamil X (Twitter) Comments at DravidianLangTech@ACL 2026. We propose a hybrid architecture that integrates contextual representations from XLM-RoBERTa with lexical TF-IDF features and explicit sarcasm-aware emoji features. Domain-specific hashtag expansion is incorporated to enrich political context. To address class imbalance, we apply inverse-frequency class weighting and label smoothing during training. Experimental results demonstrate that hybrid feature fusion significantly improves performance over transformer-only baselines. Our final system achieves a Macro-F1 score of 0.3559 on the official test set, securing Rank 10 among participating teams. The results highlight the effectiveness of combining semantic, lexical, and pragmatic cues for fine-grained political sentiment classification in Tamil.

TamilEcho_Political@DravidianLangTech 2026: Hybrid XLM-RoBERTa with Sarcasm-Aware Feature Fusion for Political Multiclass Sentiment Analysis in Tamil X

Analyzing political sentiment in code-mixed Tamil-English presents significant challenges due to informal jargon, severe class imbalance, and distribution shifts. This paper describes our system for the Political Multiclass Sentiment Analysis shared task at DravidianLangTech@ACL 2026, which categorizes tweets into seven sentiment classes. Our approach leverages XLM-RoBERTa integrated with Low-Rank Adaptation (LoRA). To mitigate majority-class dominance, we combine random oversampling with automated hyperparameter optimization to improve macro-level balance within this Parameter-Efficient Fine-Tuning (PEFT) framework. Enhanced by targeted preprocessing—specifically emoji demojization and noise removal—our system helps preserve nuanced symbolic cues, achieving a macro-average F1-score of 0.3763 and securing Rank 2 on the shared task leaderboard.

PhucNguyen@DravidianLangTech 2026: Political Multiclass Sentiment Analysis with XLM-RoBERTa and Low-Rank Adaptation

Stance and target detection in multimodal political memes presents notable challenges in low-resource and highly imbalanced settings. This task is based on the Malayalam dataset from the DravidianLangTech 2026 Shared Task(500 samples with a 95.4:4.6 stance imbalance).The primary challenges stem from linguistic variability and visually complex meme formats,which hinder accurate text extraction and effective multimodal alignment. A lightweight yet high-performing multimodal framework is proposed that integrates bilingual OCR, a Vision Transformer (ViT), and IndicBERT to learn complementary visual and textual representations. A gated fusion mechanism effectively combines multimodal features, while asymmetric loss weighting and post-training threshold optimization address extreme class imbalance. The methodology achieves a Weighted F1-score of 0.9535 for stance detection and 0.5283 for target identification, demonstrating strong robustness and generalization under realistic multimodal constraints.

A Gated Multimodal Architecture for Multi-Level Stance and Target Detection in Malayalam Political Memes.

Tamil has a lot of internal variability, including the way it is used in casual conversations, code mixing, and phonetic differences in the way it is spoken in different regions, making it quite difficult to transcribe the spoken word and classify the dialects. In order to address these challenges, our paper presents the system developed by the CUET_InferX team for the Shared Task on Dialect Based Speech Recognition and Classification in Tamil, which was part of DravidianLangTech@ACL 2026. For Subtask 2 (ASR), our proposed system is based on a dual-architecture design that incorporates a fine-tuned Whisper-large-v3 model with Low-Rank Adaptation (LoRA) and a Wav2Vec2 XLSR-53 model, topped with a KenLM statistical language model for n-gram phonetic correction. Our ASR system resulted in a Word Error Rate (WER) of 0.54, which earned us 2nd position for Subtask 2. For Subtask 1 (Speech-Based Dialect Classification), our proposed system is based on a text-based weighted ensemble of IndicBERT, MuRIL, XLM-RoBERTa, and TamilBERT models, which is completely dependent on our ASR system's transcription outputs. Our proposed system achieved a Macro F1 score of 0.22, which earned us 9th position for Subtask 1.

CUET_InferX@DravidianLangTech 2026: Shared Task on Dialect Based Speech Recognition and Classification in Tamil

The low-resource dialectal Automatic Speech Recognition (ASR) in languages like Tamil is a critical issue because of phonological differences, lack of labeled data and because of the differences in the acoustic of speech patterns among regions. This paper will introduce a dialect-conscious Tamil ASR model that is trained on the Conformer-CTC-BPE-Large framework via the NVIDIA NeMo framework. This model is an integration of convolutional subsampling, multi-head self-attention, and Connectionist Temporal Classification (CTC) decoding along with a BPE tokenizer to make possible both efficient end-to-end speech recognition. The system is tested on the audio recordings of dialectal Tamil, in which mono-channel audio normalization and batch transcription are used. Our findings indicate that even using large pretrained Conformer models, dialectal ASR tasks are successfully implemented even in zero-shot. Transcriptions generated are examined and the challenges associated with the dialectal differences and acoustic models, and we comment on the possible future directions of enhancing data-efficient adaptation in low-resource speech recognition.

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