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Hope speech detection is an important task in understanding emotionally constructive communication in online platforms, especially in low-resource and code-mixed languages. This paper describes our system submitted to the first shared task on Hope Speech Detection in Code-Mixed Tulu, organized by DravidianLangTech@ACL 2026. The shared task consists of two tasks: Task 1 - Coarse-Grained Hope Tone Classification and Task 2 - Fine-Grained Hope Type Classification, with the objective of detecting and classifying the tone and type of hope expressed in code-mixed Tulu texts. We experimented with Logistic Regression (LR) and Linear Support Vector Classifier (LinearSVC) - classical Machine Learning (ML) approaches, trained with Term Frequency and Inverse Document Frequency (TF-IDF) of word ngrams (n = 1, 2). For Task 1, we employed both models, whereas for Task 2, we employed only the LR model. Linear SVC obtained a macro F1-score of 0.51 in Task 1 and secured 4th rank, while the LR model obtained a macro F1-score of 0.37 in Task 2 and secured 5th rank. The results demonstrate that traditional ML approaches remain effective for low-resource code-mixed language scenarios.

ACL 2026

SJM_MINDS@DravidianLangTech@ACL2026: Machine Learning Approaches for Hope Speech Detection in Code-Mixed Tulu

code-mixed tulu

social media analysis

low-resource languages

machine learning

natural language processing

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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workshop paper

This paper describes the system that our Still-Loading team designed to run the Telugu Prompt-Style Recovery shared task at DravidianLangTech@ACL 2026. The purpose of the given task is categorizing Telugu transcript passages as belonging to one of 9 communicative styles: Formal, Informal, Optimistic, Pessimistic, Humorous, Serious, Inspiring, Authoritative, and Persuasive. We compared several multilingual Transformer-based models, i.e. MuRIL, XLM-RoBERTa-Large, mBERT, and IndicBERTv2. We chose a "Turbo Sandwich" preprocessing strategy which helps to give more emphasis to lexical deltas, in addition to Focal Loss. Our system based on the MuRIL was rated at the 7th place in the official leaderboard with a Macro-F1 rating of 0.1703. The source code to reproduce our experiments is publicly available on Still-Loading-Prompt-Recovery-for-LLM-in-Telugu (https://github.com/Priyontee1713/Still-Loading-Prompt-Recovery-for-LLM-in-Telugu).

Still-Loading@DravidianLangTech 2026: Telugu Prompt-Style Recovery using Multilingual Transformers

Multilingual transformers have achieved re- markable performance on code-mixed senti- ment benchmarks, but their robustness under linguistic stress and domain shift remains un- derexplored. We fine-tune XLM-RoBERTa and mBERT on a carefully cleaned 25,543- tweet Hinglish sentiment dataset, where XLM- R achieves near-perfect in-distribution accu- racy (99.7%). The integrity of this result is confirmed by rigorous hash-based and 3-gram Jaccard deduplication, ruling out data leakage. However, when evaluated on a 400-example human-validated adversarial benchmark span- ning negation, sarcasm, contrast, subtle senti- ment, and true neutral, XLM-R performance collapses to 42.5% -- a drop of over 57 per- centage points. Zero-shot transfer to English TweetEval yields only 50.8% accuracy (40.8% macro F1), above . Our results highlight a crit- ical gap between benchmark scores and real- world reliability, underscoring the need for ad- versarial evaluation and cross-domain stress- testing before deploying sentiment models in practical, safety-sensitive applications.

99.7% Accuracy Is Not Enough: Adversarial Vulnerability of Hinglish Sentiment Models

This paper introduces Thiruppugazh-KG, a semantically annotated dataset and knowledge graph derived from the Thiruppugazh corpus, a 14th-century collection of 1,335 Tamil devotional hymns composed by Arunagirinathar. The dataset includes annotations for entities, devotional themes, mythological events, philosophical concepts, imagery, and sacred locations mentioned in each hymn. Using these annotations, we construct a Neo4j-based knowledge graph that models relationships between hymns and their associated cultural and narrative elements. Graph analytics, including PageRank, are applied to identify prominent entities and sacred locations within the corpus. The resulting resource provides a structured representation of Tamil devotional literature and supports computational analysis of cultural texts in low-resource languages.

Thiruppugazh-KG Dataset: A Manually Annotated Resource for Computational Analysis of Tamil Devotional Literature

Our paper explores several machine learning methods for detecting toxic language in gaming-related chat utterances. We start with the GameTox dataset, perform some data preprocessing and augment the minority classes with LLM-generated synthetic data. We then set a baseline using a classic Logistic Regression model and continue to explore several approaches to surpassing it, by leveraging the leading multilingual transformer models (XLM-RoBERTa and DeBERTa-V3) to classify our test data. We achieve a top result of 0.6725 Macro-F1 (2nd place on shared task leaderboard) using a MDeBERTa-V3 model which we pretrained on the Jigsaw dataset for 1 epoch and then fine-tuned on our GameTox data for 5 epochs.

FNLP412@EEUCA 2026: Understanding Toxic Behavioral Intent in Gaming Chat Logs using Transfer Learning and Synthetic Data Augmentation

Vaccine stance detection in multimodal memes has emerged as an important yet challenging task, requiring models to interpret both textual and visual cues that jointly convey opinions. The difficulty lies in capturing subtle semantic interactions and handling class imbalance across stance categories. In this paper, we present our system developed for the VaxMeme 2026 Shared Task at EEUCA 2026. Our approach leverages a soft-voting ensemble of complementary models, combining DeBERTa-v3-large and RoBERTa-large for rich textual representation with CLIP (ViT-B/32) for joint vision-language understanding. We incorporate domain-specific preprocessing, techniques such as random token deletion, image enhancement, and balanced class oversampling to address dataset limitations. Through extensive ablation studies, we identify balanced class oversampling as the most effective component, significantly improving performance across models. Our final system achieves a macro F1-score of 0.8306, securing 8th place among 25 teams, demonstrating the effectiveness of ensemble-based multimodal learning for stance detection.

Quasar@EEUCA 2026: Multimodal Deep Learning for Vaccine Stance Detection in Memes

Vaccine-critical memes have emerged as a growing challenge for public health communication, combining images and text to spread misinformation in ways that are difficult to detect automatically. In this paper, we have described our system for the EEUCA 2026 Shared Task on Multimodal Vaccine-Critical Meme Detection, classifying memes from the VaxMeme dataset into Vaccine-Critical, Neutral and Pro-Vaccine categories. We have experimented with multiple text encoders and visual backbones, finding that Twitter-RoBERTa fused with CLIP ViT-L/14 through gated cross-modal attention has achieved a test macro F1 of 0.8357. We have further shown that domain-specific pretraining has outperformed larger general-purpose models, highlighting the importance of domain adaptation over raw model scale. Finally, our system has secured the 3rd position on the shared task leaderboard.

CUET_SYNTHETICA@EEUCA 2026: Gated Cross-Modal Attention with Domain-Adapted Text Encoding for Vaccine-Critical Meme Detection

Memes on social media have emerged as a crucial medium for disseminating vaccine-related viewpoints, yet their inherent irony, metaphor, and text-image misalignment pose significant challenges to automatic detection. In this paper, we propose MoEs-VaxAgent, a two-stage multimodal framework for vaccine critical meme detection. First, we design a dynamic routing Mixture-of-Experts module capable of adaptively capturing multi-granular semantic cues within memes. Second, to address hard samples located at the decision boundaries, we introduce an uncertainty-aware multi-agent rectification mechanism to perform a secondary detection on samples identified with low confidence in the first stage. In the EEUCA 2026 Shared Task on Multimodal Vaccine Critical Meme Detection, our system achieved a Macro F1-score of 0.8205, ranking 9th on the official leaderboard. Furthermore, we discuss various exploratory strategies evaluated during the competition and provide a detailed analysis of the model's performance.

wenbin@EEUCA 2026: MoEs-VaxAgent, A Two-Stage Framework for Multimodal Vaccine Critical Meme Detection

This paper describes our system for the EEUCA 2026 Shared Task on toxicity classification in gaming chat. We implement a three-stage pipeline combining an ensemble of two compact transformers (DeBERTa-v3-base,184M;XLM-RoBERTa-base,278M) with a Linguistically-Informed Mediator (LIM) that resolves inter-model disagreements through corpus-backed lexical normalization,class-conditional unigram scoring,multilingual profanity detection,and agentive targeting analysis grounded in speech act theory.The LIM specifically targets the minority classes(Hate & Harassment,Threats,and Extremism),which are the most safety-critical categories in real-world gaming moderation.To address the extreme class imbalance(1,450:1 Non-toxic to Extremism ratio),we introduce a two-stage data augmentation strategy using only the provided training data.Our system achieves a Macro F1 of 0.6441 and accuracy of 0.9062 on the official test set,ranking 3rd in Macro F1 and 1st in accuracy among all teams.The proposed pipeline is domain-portable:adapting to other gaming platforms requires substituting only the game-specific entity lexicon.Code is publicly available at https://github.com/Anmol2059/thaulab_EEUCA.

thaulab@EEUCA 2026: Who Said What to Whom? A Targeting-Aware Neural-Symbolic Pipeline for Gaming Toxicity Detection

Event extraction for low-resource languages such as Vietnamese is limited by the lack of large-scale annotated data. To address this, we propose a weakly supervised framework that constructs a silver corpus via pseudo-labeling. We introduce a cross-document n-ary relation filtering strategy to reduce noise by leveraging consistency across multiple articles describing the same event, and further enhance data diversity with schema-based augmentation. Experiments on the BKEE benchmark show consistent improvements, demonstrating the effectiveness of our approach. Data is available at: \url{https://github.com/Larken1612/VietEE2}.

Constructing a Silver Corpus for Weakly Supervised Vietnamese Event Extraction using Cross-Document N-ary Relation Filtering

This paper presents two systems for the Game-Tox Shared Task at the Workshop on EEUCA at ACL 2026, which requires classifying World of Tanks chat utterances into six fine-grained toxic intent categories (Labels 0–5). Severe class imbalance, domain-specific multilingual slang, and extremely scarce data for rare categories such as Threats (Label 4, 60 samples) and Extremism (Label 5, 24 samples) make this a challenging classification problem. Our primary submission, RAKSHAK (rak s. aka, Sanskrit for “Protector”), is a multi-task DeBERTa-v3-base (He et al., 2022) framework combining rationale distillation from Qwen2.5-14B (An et al., 2024), Supervised Contrastive Loss, and dedicated rare-class binary heads. RAKSHAK’s training data is augmented with cross-domain transfer from the Jigsaw Toxic Comment dataset (16,225 samples mapped to Labels 1–4) and 100 LLM-generated extremism samples for Label 5. Our secondary system (M1) fine-tunes DeBERTa-v3-base with Focal Loss on the original GameTox data plus the same 100 extremism samples, without Jigsaw transfer. RAKSHAK achieves a Macro F1 of 0.5883 on the official test set, ranking 7th out of 35 participating teams, while M1 achieves 0.5252 Macro F1. An ablation comparing M1 with and without Jigsaw data shows that cross-domain transfer accounts for +2.6 F1 points, while RAKSHAK’s multi-task architecture contributes a further +3.7 points.

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