ZeroR@CHiPSAL 2026: Two-Stage Vision-Language Adaptation with Contrastive Learning for Nepali Meme Classification
arXiv:2607.28637v1 Announce Type: new Abstract: This paper presents our system for the CHiPSAL 2026 shared task on multimodal hate speech and sentiment detection in Nepali memes. We address both subtasks: binary hate speech classification and three-class sentiment analysis. Our approach adapts the Robust Adaptation of Hateful Meme Detection (RA-HMD) framework using Qwen3-VL-8B-Instruct, a state-of-the-art vision-language model with native Devanagari support. We employ a two-stage training pipeline: (1) LoRA fine-tuning with an MLP projection head for generative classification, and (2) contrastive backbone fine-tuning with supervised InfoNCE loss. We handle class imbalance through minority oversampling, image augmentation, and focal loss. At inference, we ensemble Stage 1 token probabilities with Stage 2 classifier scores using validation-tuned weights. Our end-to-end approach eliminates error propagation from separate OCR and translation pipelines by leveraging the model's native Devanagari understanding. Our system achieved \textbf{2nd place} on hate speech detection (F1: 0.797) and \textbf{4th place} on sentiment analysis (F1: 0.518). We provide detailed ablations, error analysis, and insights into adapting large vision-language models for low-resource South Asian languages.
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[Submitted on 19 May 2026]
Title:ZeroR@CHiPSAL 2026: Two-Stage Vision-Language Adaptation with Contrastive Learning for Nepali Meme Classification
View a PDF of the paper titled ZeroR@CHiPSAL 2026: Two-Stage Vision-Language Adaptation with Contrastive Learning for Nepali Meme Classification, by Nitiz Khanal
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Abstract:This paper presents our system for the CHiPSAL 2026 shared task on multimodal hate speech and sentiment detection in Nepali memes. We address both subtasks: binary hate speech classification and three-class sentiment analysis. Our approach adapts the Robust Adaptation of Hateful Meme Detection (RA-HMD) framework using Qwen3-VL-8B-Instruct, a state-of-the-art vision-language model with native Devanagari support. We employ a two-stage training pipeline: (1) LoRA fine-tuning with an MLP projection head for generative classification, and (2) contrastive backbone fine-tuning with supervised InfoNCE loss. We handle class imbalance through minority oversampling, image augmentation, and focal loss. At inference, we ensemble Stage 1 token probabilities with Stage 2 classifier scores using validation-tuned weights. Our end-to-end approach eliminates error propagation from separate OCR and translation pipelines by leveraging the model's native Devanagari understanding. Our system achieved \textbf{2nd place} on hate speech detection (F1: 0.797) and \textbf{4th place} on sentiment analysis (F1: 0.518). We provide detailed ablations, error analysis, and insights into adapting large vision-language models for low-resource South Asian languages.
Comments: 9 pages, 2 figures, system description paper for the CHiPSAL 2026 shared task at LREC 2026
Subjects:
Computation and Language (cs.CL)
ACM classes: I.2.7; I.5.1; I.4.0
Cite as: arXiv:2607.28637 [cs.CL]
(or arXiv:2607.28637v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.28637
arXiv-issued DOI via DataCite
Journal reference: Proceedings of the Second Workshop on Challenges in Processing South Asian Languages (CHiPSAL 2026) @ LREC 2026, pages 275-283, Palma, Mallorca, Spain, 16 May 2026. ELRA Language Resources Association (ELRA). ISBN 978-2-493814-66-1
Submission history
From: Nitiz Khanal [view email] [v1] Tue, 19 May 2026 15:14:58 UTC (31 KB)
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