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 Whisper-large-v3 (1.5B) by 8 WER points despite having half the parameters.

This paper describes Team CHMOD_777’s system for the DravidianLangTech@ACL 2026 shared task on political multiclass sentiment analysis of Tamil Twitter comments. The task requires classifying Tamil political tweets into seven sentiment categories under severe class imbalance (8:1 ratio). We address this challenge through LLM-based data augmentation using Gemini 2.5 Flash, expanding training data from 4,352 to 15,316 samples (3.5x the original). Our best system, MuRIL fine-tuned on augmented data with Focal Loss (gamma=3.0) and weighted sampling, achieves 35.79% Macro F1 on the development set, a 67% relative improvement over the non-augmented baseline. On the official test set, our system achieves 34.25% Macro F1, ranking 12th out of 22 participating teams. We find that (1) language-specific pre-training (MuRIL, 236M) outperforms larger general models (IndicBERT-v3, 1B), (2) smaller models benefit disproportionately from augmentation, and (3) Substantiated is the hardest category (F1=10.7%) due to its requirement for factual reasoning.

This paper describes Team CHMOD_777’s system for the DravidianLangTech@ACL 2026 shared task on detecting abusive Tamil text targeting women on social media. We fine-tune three transformer backbones (MuRIL, XLM-RoBERTa, IndicBERT-v3) with Focal Loss and weighted sampling, systematically evaluating the effects of context length, hyperparameter tuning, and language-specific pre-training. Our best system, MuRIL with 256-token context, achieves 82.76% Macro F1 on the development set and 80.61% on the official test set, ranking 6th out of 24 teams. We find that (1) extending context from 128 to 256 tokens improves F1 while converging 2.4x faster, (2) language-specific pre-training (MuRIL, 236M) outperforms larger models (IndicBERT, 270M), and (3) default hyperparameters are optimal, with every tuning attempt degrading performance.

Political multiclass detection is the task of identifying the predefined seven political classes. In this paper, we report an overview of the findings on the “Political Multiclass Sentiment Analysis of Tamil X(Twitter) Comments” shared task conducted at the workshop on DravidianLangTech@NAACL 2025. The participants were provided with annotated Twitter comments, which are split into training, development, and unlabelled test datasets. A total of 139 participants registered for this shared task, and 25 teams finally submitted their results. The performance of the submitted systems was evaluated and ranked in terms of the macro-F1 score.

This paper illustrates the overview of the sharedtask on automatic speech recognition in the Tamillanguage. In the shared task, spontaneousTamil speech data gathered from elderly andtransgender people was given for recognitionand evaluation. These utterances were collected from people when they communicatedin the public locations such as hospitals, markets, vegetable shop, etc. The speech corpusincludes utterances of male, female, and transgender and was split into training and testingdata. The given task was evaluated using WER(Word Error Rate). The participants used thetransformer-based model for automatic speechrecognition. Different results using differentpre-trained transformer models are discussedin this overview paper.

This paper presents the findings of the shared task on Multimodal Sentiment Analysis and Troll meme classification in Dravidian languages held at ACL 2022. Multimodal sentiment analysis deals with the identification of sentiment from video. In addition to video data, the task requires the analysis of corresponding text and audio features for the classification of movie reviews into five classes. We created a dataset for this task in Malayalam and Tamil. The Troll meme classification task aims to classify multimodal Troll memes into two categories. This task assumes the analysis of both text and image features for making better predictions. The performance of the participating teams was analysed using the F1-score. Only one team submitted their results in the Multimodal Sentiment Analysis task, whereas we received six submissions in the Troll meme classification task. The only team that participated in the Multimodal Sentiment Analysis shared task obtained an F1-score of 0.24. In the Troll meme classification task, the winning team achieved an F1-score of 0.596.