Transformer-Based Ensemble Models Improve Hate Speech Detection and Sentiment Analysis in Nepali Memes
A research team developed and evaluated transformer-based NLP models for detecting hate speech and analyzing sentiment in Nepali-language internet memes. The study addressed challenges unique to Nepali memes, including frequent code-mixing and scarce baseline resources, by extracting meme text via OCR before applying six distinct models. The findings offer practical guidance on when ensemble versus standalone models are preferable for low-resource, multilingual content moderation tasks.
Researchers from TeamHerald, presenting at the CHiPSAL 2026 workshop at LREC 2026, tackled the underexplored problem of automated hate speech detection and sentiment analysis in Nepali internet memes. Because memes blend visual and textual content, the team adopted a text-centric pipeline that first extracts embedded text using an OCR layer before feeding it into transformer-based language models. Six distinct models were evaluated across two tasks: binary hate speech detection and three-class sentiment analysis. For binary classification, a standalone decoder-only model achieved the best performance, while a Soft Voting ensemble outperformed all individual models on the multi-class sentiment task, delivering a 15.8% relative improvement in Macro F1-score over the strongest standalone baseline. The study concludes that ensemble aggregation strategies behave differently depending on whether the task is binary or multi-class, underscoring the need to match the aggregation method to the classification objective. The work contributes to the growing body of research on low-resource South Asian language processing, where labeled datasets and established benchmarks remain scarce.
What's missing
The paper does not report the size or composition of the dataset used, the specific transformer architectures evaluated by name, or inter-annotator agreement statistics for the hate speech and sentiment labels — all of which are important for assessing reproducibility and generalizability. The study also does not address whether the OCR layer introduces systematic errors for Nepali script or code-mixed text, which could affect downstream model performance.
What different sources said
- arXiv cs.AICenter
TeamHerald@CHIPSAL 2026: Hate Speech Detection and Sentiment Analysis of Nepali Memes using Transformer-based Architectures and Ensemble Learning
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