United States

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.

ACL 2026

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

tamil nlp

tamil spell correction

mayangoli errors

multilingual transformers

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
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NAACL 2024

2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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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.

Dialectmind@DravidianLang Tech 2026:Zero-Shot Dialectal Tamil Automatic Speech Recognition Using a Large Pretrained Conformer Model

This paper presents our submission to the Depression Detection in Dravidian Languages shared task at DravidianLangTech 2026. We investigate three complementary modeling paradigms for speech-based depression detection in Tamil and Malayalam: (i) acoustic feature engineering using MFCC and prosodic features with a Support Vector Machine (SVM) classifier, (ii) an image-based convolutional neural network (CNN) trained on Mel-spectrogram representations, and (iii) a text-based transformer model leveraging Whisper-generated transcripts fine-tuned with XLM-RoBERTa. Experimental results demonstrate that transformer-based textual modeling substantially outperforms acoustic-only approaches, highlighting the strong discriminative power of lexical information in the dataset. We provide a comparative analysis of the three approaches and discuss limitations and future research directions.

SERENE@DravidianLangTech 2026: Multimodal Approaches for Depression Detection in Dravidian Speech: Acoustic, Spectrogram, and Transformer-Based Models

The rapid growth of social media networks faces challenges in the classification of mul tilingual and code-mixed data.A task is shared by Political Multiclass Sentiment Analysis of Tamil X (Twitter)-DravidianLangTech@ACL 2026 to classify the political text.For the above task, we proposed two solutions to compare a traditional machine learning and the trans former based model.First we developed a Baseline traditional Support vector Machine model using the TF-IDF features.To capture the deeper information from the text we devel oped an XLM-RoBERT model where we used minimal pre-processing technique.The Result shows us that the transformer-based performs well compared to the traditional baseline model with the macro F1 score of 0.3738.The Study highlights the importance of robust multi-class social media political text classification.

Trailblazer@DravidianLangTech 2026:A Comparative Study of TF-IDF SVM and XLM-RoBERTa for Political Multiclass Text Classification.

Tamil is a pre-historic language of millions of individuals who live in India, Sri Lanka, and other parts of the world. Consider the variations in accents, vocabulary and even speech rhythm even among the central region, the northern region, the southern region and the western region of Tamil Nadu. Such idiosyncrasies make it difficult to use features such as voice assistants or translation applications to keep up. A feasible system has been developed in this project to manage that challenge. It picks up raw audio files in Tamil, identifies which of the four predominant dialects the speech belongs to and translates that speech into text. Good quality datasets on Tamil dialects are rather rare, due to the lack of resources and interest in languages. There were pre-trained models, namely, XLSR to spot the dialects and Wav2Vec 2.0 to convert speech into text. All in all, this configuration had an accuracy rate of 46 percentage. It was very good at distinguishing between northern and southern, but was somewhat confused between central and west-central-western. In the case of the transcription component, a cursory inspection reveals that it is a reliable process, able to nail down clear speech despite those accent twists. With that said, it is possible to improve it with such details as a more detailed fine-tuning or equalizing the classes of data.

Azrael@DravidianLangTech 2026:Dialect-Sensitive Automatic Speech Recognition and Classification for Tamil

Depression is a major mental health concern that can be reflected through subtle changes in speech patterns, prosody, and vocal characteristics. In low-resource and multilingual settings, depression detection from speech may become particularly more challenging. In this work, we present our system for the Shared Task on Depression Detection from Malayalam and Tamil. We explored both handcrafted acoustic features (MFCC) and pretrained speech representations (Wav2Vec2) for depression detection, along with a simple fusion strategy to examine their complementary strengths. Our observations showed that Wav2Vec2 generalized better for Malayalam, whereas for Tamil, a validation-tuned probability fusion performed best. The final system achieved macro-F1 scores of 99.5% for Malayalam and 88.6% for Tamil, securing 3rd place in both tasks.

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