@inproceedings{regmi-etal-2026-linus,
    title = "linus@{CH}i{PSAL} 2026: Multimodal Hate Speech and Sentiment Detection in Low-Resource Memes Using Late-Fusion Hybrid Architecture",
    author = "Regmi, Sunil  and
      Subedi, Bipesh  and
      Singh, Saugat  and
      Shrestha, Suman",
    editor = "Sarveswaran, Kengatharaiyer  and
      Vaidya, Ashwini",
    booktitle = "Proceedings of the Second workshop on Challenges in Processing {S}outh {A}sian Languages ({CH}i{PSAL}2026)",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/revision-workflow/2026.chipsal-1.27/",
    doi = "10.63317/3t7nocwwhq8r",
    pages = "267--274",
    abstract = "The increased sharing of memes on social media creates serious challenges for automated moderation, especially in low-resource and code-mixed languages such as Nepali. In this paper, we present our system for the CHiPSAL 2026 Shared Task on Multimodal Hate and Sentiment Understanding in Low-Resource Memes. We propose a late-fusion hybrid architecture that combines OpenAI{'}s Vision Transformer (CLIP ViT-B/32) with a domain-specific Nepali language model (NepBERTa) to capture both visual features and linguistic information. To address data scarcity, we introduce a cross-task label mapping and data augmentation strategy between the hate speech and sentiment datasets. By applying controlled hyperparameter settings and balanced loss optimization, our framework achieved a Macro F1 score of 0.8052 on Subtask A (Hate Speech Detection) and 0.6881 on Subtask B (Sentiment Analysis) in the official CodaBench evaluation, demonstrating the effectiveness of the proposed multimodal approach."
}