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        <title>linus@CHiPSAL 2026: Multimodal Hate Speech and Sentiment Detection in Low-Resource Memes Using Late-Fusion Hybrid Architecture</title>
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        <namePart type="given">Sunil</namePart>
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        <namePart type="family">Singh</namePart>
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    <name type="personal">
        <namePart type="given">Suman</namePart>
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            <title>Proceedings of the Second workshop on Challenges in Processing South Asian Languages (CHiPSAL2026)</title>
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            <namePart type="given">Kengatharaiyer</namePart>
            <namePart type="family">Sarveswaran</namePart>
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            <namePart type="given">Ashwini</namePart>
            <namePart type="family">Vaidya</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T linus@CHiPSAL 2026: Multimodal Hate Speech and Sentiment Detection in Low-Resource Memes Using Late-Fusion Hybrid Architecture
%A Regmi, Sunil
%A Subedi, Bipesh
%A Singh, Saugat
%A Shrestha, Suman
%Y Sarveswaran, Kengatharaiyer
%Y Vaidya, Ashwini
%S Proceedings of the Second workshop on Challenges in Processing South Asian Languages (CHiPSAL2026)
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma de Mallorca, Spain
%F regmi-etal-2026-linus
%X 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.
%R 10.63317/3t7nocwwhq8r
%U https://aclanthology.org/2026.chipsal-1.27/
%U https://doi.org/10.63317/3t7nocwwhq8r
%P 267-274

Markdown (Informal)

[linus@CHiPSAL 2026: Multimodal Hate Speech and Sentiment Detection in Low-Resource Memes Using Late-Fusion Hybrid Architecture](https://aclanthology.org/2026.chipsal-1.27/) (Regmi et al., CHiPSAL 2026)

• linus@CHiPSAL 2026: Multimodal Hate Speech and Sentiment Detection in Low-Resource Memes Using Late-Fusion Hybrid Architecture (Regmi et al., CHiPSAL 2026)

ACL

• Sunil Regmi, Bipesh Subedi, Saugat Singh, and Suman Shrestha. 2026. linus@CHiPSAL 2026: Multimodal Hate Speech and Sentiment Detection in Low-Resource Memes Using Late-Fusion Hybrid Architecture. In Proceedings of the Second workshop on Challenges in Processing South Asian Languages (CHiPSAL2026), pages 267–274, Palma de Mallorca, Spain. ELRA Language Resources Association (ELRA).