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BioSentinel uses XLM-RoBERTa for sexism intent classification in memes

By PulseAugur Editorial · [1 sources] ·

The BioSentinel team developed a text-centric approach for classifying sexist intent in memes for the EXIST 2026 competition. Their system, built on XLM-RoBERTa-base, utilized a combined loss function incorporating KL divergence for soft labels and weighted cross-entropy for hard labels. This method achieved competitive rankings in both soft-label and hard-label evaluations, demonstrating the effectiveness of their approach to subjective NLP tasks. AI

IMPACT This research contributes to the development of models capable of understanding nuanced intent in multimodal content, potentially improving content moderation and analysis tools.

RANK_REASON The item is an academic paper detailing participation in a specific task within an evaluation campaign, presenting a novel approach and results. [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 →

BioSentinel uses XLM-RoBERTa for sexism intent classification in memes

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The item is an academic paper detailing participation in a specific task within an evaluation campaign, presenting a novel approach and results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Chandru Munisamy, Karthikeya Raguveer, Alapan Kuila ·

    BioSentinel at EXIST 2026: Soft-Label Optimization with XLM-RoBERTa for Sexism Intent Classification in Memes

    arXiv:2607.24137v1 Announce Type: new Abstract: This paper describes the BioSentinel team's participation in EXIST 2026 Task 2.2: Source Intention in Memes, part of the CLEF 2026 evaluation campaign. The task requires classifying the communicative intent behind memes as direct, j…

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