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RAKSHAK system achieves 7th place in toxic intent classification at ACL 2026

By PulseAugur Editorial · [1 sources] ·

Researchers have developed two systems, RAKSHAK and M1, for the GameTox Shared Task at ACL 2026, focusing on classifying toxic intent in World of Tanks chat. RAKSHAK, the primary system, utilizes a DeBERTa-v3-base model enhanced with rationale distillation from Qwen2.5-14B, Supervised Contrastive Loss, and dedicated heads for rare classes. It also incorporates cross-domain transfer from the Jigsaw Toxic Comment dataset and LLM-generated samples for extremism. RAKSHAK achieved a Macro F1 of 0.5883, ranking 7th out of 35 teams, while the secondary system M1 achieved 0.5252 Macro F1. AI

IMPACT This research demonstrates advanced techniques for handling imbalanced and scarce data in toxic intent classification, potentially improving moderation tools.

RANK_REASON The item is an academic paper detailing a system for a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

RAKSHAK system achieves 7th place in toxic intent classification at ACL 2026

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The item is an academic paper detailing a system for a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Binayak Karki, Aryan Kafle, Pingala Ghimire ·

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

    arXiv:2607.20450v1 Announce Type: new Abstract: This paper presents two systems for the GameTox 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 imb…

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