@inproceedings{acharya-etal-2026-teamherald,
    title = "{T}eam{H}erald@{CHIPSAL} 2026: Hate Speech Detection and Sentiment Analysis of {N}epali Memes Using Transformer-based Architectures and Ensemble Learning",
    author = "Acharya, Ashish  and
      Khatiwada, Anish  and
      Khadka, Rohit  and
      Aryal, Pragya",
    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-1.24/",
    doi = "10.63317/328gg52ap2qp",
    pages = "244--249",
    abstract = "The analysis of internet memes in the Nepali language is complicated by frequent code-mixing and a lack of established baseline resources. While memes inherently combine visual and textual elements, this study focuses on a text-centric approach by extracting embedded text using an OCR layer and modeling it with Transformer-based architectures. We evaluate six distinct models and investigate the comparative effectiveness of Hard and Soft Voting ensemble strategies across two tasks: binary hate speech detection and three-class sentiment analysis. Experimental results show that a standalone decoder-only model achieved the highest performance for binary classification, whereas the Soft Voting ensemble performed best for the multi-class sentiment task, yielding a 15.8{\%} relative improvement in Macro F1-score over the strongest standalone baseline. These findings suggest that ensemble strategies behave differently across binary and multi-class tasks, highlighting the importance of selecting aggregation methods suited to the classification objective."
}