United States

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.

ACL 2026

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

gaming chat

toxic language

data augmentation

transfer learning

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

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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.

ShriNep@EEUCA 2026: RAKSHAK – Multi-Task DeBERTa with Rationale Distillation and Jigsaw-Augmented Training for Toxic Intent Classification

Research on Event Extraction (EE) in South Asian languages is crucial for understanding information dissemination and enabling automated news analysis in morphologically complex, low-resource environments. To address the scarcity of high-quality, publicly available datasets, we present Nepali Event Extraction (NepEE), a manually annotated corpus comprising 10,226 Devanagari sentences. The dataset includes annotations for trigger spans and event types, achieving high inter-annotator agreement with Fleiss’ kappa = 0.812 for trigger identification and kappa = 0.855 for event classification. Our dataset was developed through a rigorous iterative three-phase protocol involving five expert native speakers to ensure linguistic precision. We conduct benchmarking across a broad spectrum of approaches, including classical feature-based models, five fine-tuned Transformer encoders, and contemporary instruction-tuned Large Language Models (LLMs) using zero-shot and fixed few-shot prompting. Our analysis shows that Indic-specialized Transformers achieve superior classification performance, while traditional methods and few-shot prompting struggle with the challenges of exact span extraction in morphologically complex contexts. Furthermore, we quantify performance differences between sentence-level and span-level tasks, providing strong baselines for future research. The findings and the released NepEE dataset provide a valuable resource for advancing event understanding in low-resource languages (LRLs). All code and resources are available at https://github.com/SUJAL390/EEUCA-ACL-2026-Trigger-Phrase-Identification-and-Event-Classification-in-Low-Resource-Languages.

Benchmarking Models for Low-Resource Nepali Event Extraction with Trigger Phrase Identification and Event Classification

This paper describes our system for the EEUCA 2026 Shared Task on Understanding Toxic Behavior in Gaming Communities. The task involves classifying World of Tanks chat messages into six toxicity categories: Non-toxic, Insults/Flaming, Other Offensive, Hate/Harassment, Threats, and Extremism. We explore multiple approaches including encoder-based models, instruction-tuned LLMs with LoRA fine-tuning, hierarchical classification, one-vs-rest strategies, and various ensemble methods. Our best system combines Llama 3.1 8B with carefully calibrated 5% synthetic data augmentation, achieving an F1-macro score of 0.6234 on the test set, placing 4th out of 35 participating teams. We provide extensive analysis of the dataset's annotation patterns and their impact on model generalization, revealing a critical ``validation trap'' phenomenon where high validation performance correlates with poor test transfer.

PSK@EEUCA 2026: Fine-tuning Large Language Models with Synthetic Data Augmentation for Multi-class Toxicity Detection in Gaming Chat

Online gaming involves large amount of people forming a large community of players who interact in real time. Toxic behavior in online chat is common and can harm players by deterring them. Thus, automated moderation is a necessity but difficult because game chat mixes domain-specific slang, deliberate obfuscation, informal "gamer" language , and tiny support for categories such as threats and extremism. This paper describes the TAGA (Token-Attribution Guided Attention) system submitted to the EEUCA 2026 Shared Task on Understanding Toxic Behavior in Gaming Communities. We propose TAGA, an architecture that employs a leave-one-out attribution method using the Detoxify toxicity scorer to compute per-token attribution scores across multiple toxicity dimensions, which are then projected into the learned attention biases that steer the model toward toxicity-indicative tokens. By preparing a five phase ablation study, we demonstrate that each component: domain-specific preprocessing, focal loss with label smoothing, attribution-guided attention pooling, and dual-model Detoxify features with strategic oversampling contributes to a cumulative gain in macro-F1 score points over the DeBERTa-v3-base baseline reported. The final system achieves a test macro-F1 score of 0.618 and, importantly, produces non-zero predictions for extreme data imbalance present in the dataset used in the shared task.

TAGA@EEUCA 2026: Token-Attribution Guided Attention for Fine-Grained Toxic Behaviour Classification in Online Gaming Communities

Procedural event annotations record what changed but not the semantic relevance or grounding of the change: whether the annotated entity is the kind of thing whose state matters for the domain. We present Entity Qualia Structure (EQS), a per-entity sortal-type categorization (coarsened from Generative Lexicon's type system to three categories: natural, artifactual, instrument) extracted from existing lexical resources. Applied to the OpenPI food domain, EQS reaches 84.7% coverage of the 518-item entity vocabulary; across 9367 transformation annotations, only 51.1% concern food entities themselves, while 30.2% record state changes of instruments, entities whose sortal type places them outside the food-state task. In a three-way comparison against existing cleanup efforts, EQS uniquely flags 15.6% of annotations that neither human re-annotation (OpenPI-C) nor LLM salience scoring (OpenPI 2.0) catches. Analysis of the agentive quale reveals that 93% of agentive-positive annotations involve instruments rather than food: entity creation can only be detected when the agentive feature is paired with the associated verb's event semantics.

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