TAGA@EEUCA 2026: Token-Attribution Guided Attention for Fine-Grained Toxic Behaviour Classification in Online Gaming Communities
Year: 2026
Type: conference-paper
Abstract: 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 limited coverage 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: domainspecific 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.
Institution: Delhi Technological University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166877574