United States

Depression is one of the most common mental health problems in the world. It affects a person’s emotions, thinking, energy levels, and daily life. Early detection of depression is very important to provide timely support and treatment. While many studies focus on identifying depression from text, speech also carries important emotional and psychological signals that are often not fully explored. This paper presents an overview of the shared task on Depression Detection in Dravidian Languages (DD- DL). The task focuses on identifying signs of depression from speech data in two low-resource Dravidian languages: Tamil and Malayalam. Participants were provided with curated training datasets and were asked to build systems to classify speech samples as Depressed or Non-Depressed. The shared task includes two subtasks: (1) Depression detection in Tamil and (2) Depression detection in Malayalam. Participants applied various machine learning and deep learning approaches to model the acoustic and linguistic characteristics of speech. All submissions were evaluated using the macro-F1 score, which ensures fair performance measurement across classes

ACL 2026

Shared Task on Depression Detection from Malayalam and Tamil Speech Data

speech data

malayalam

dravidian language

depression detection

tamil

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.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
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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

Our proposal for the Dravidian LangTech 2026 Tamil Political Sentiment Analysis job is outlined in this document. Seven categories—substantiated, sarcastic, opinionated, positive, negative, neutral, and none of the above—should be used to classify Tamil political remarks according to their attitudes. Classifying the sentiments of Tamil political utterances is quite difficult. Furthermore, the emotions associated with various identities are not distributed uniformly. We built an ensemble of two transformer-based techniques, XLM-RoBERTa and IndicBERT, and used 10-fold cross-validation to improve the model’s dependability and prevent overfitting in order to address some of these issues while finishing this research. In order to help the model concentrate more on the challenging examples, used oversampling to address class imbalance and Focal Loss to train the model. In order to improve the representation of sentences, finally averaged the token embeddings.

ByteBuilders@DravidianLangTech 2026: Transformer-Based Weighted Ensemble for Political Multiclass Sentiment Analysis of Tamil X (Twitter) Comments

The rapid growth of social media platforms has led to a substantial increase in user-generated content, including abusive and offensive language. Detecting abusive content becomes particularly challenging in low-resource and code-mixed language settings such as Telugu-English social media text. Code-mixed content involves transliteration, inconsistent spelling variations, informal expressions, and frequent language switching within a single sentence. This paper focuses on detecting abusive content in Telugu-English code-mixed comments using both traditional machine learning and transformer-based deep learning models. The proposed approach incorporates preprocessing strategies to normalize transliterations and spelling variations, hybrid feature extraction techniques combining TF-IDF and FastText embeddings, and fine-tuning of multilingual transformer models. The study addresses challenges such as morphological complexity, contextual ambiguity, and limited annotated data in low-resource NLP environments.

Abusive Content Detection in Telugu-English Code-Mixed Social Media Using Hybrid Transformer Architectures

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.

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

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.

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Next from ACL 2026

ByteBuilders@DravidianLangTech 2026: Transformer-Based Weighted Ensemble for Political Multiclass Sentiment Analysis of Tamil X (Twitter) Comments

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TriVector@DravidianLangTech 2026: Depression Detection from Tamil and Malayalam Speech with Speaker-Independent Evaluation using MFCC and Wav2Vec2

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