This paper presents a system for hate speech detection in low-resource Nepali memes, submitted as part of Subtask A of the Shared Task on Multimodal Understanding at CHiPSAL 2026. Detecting hateful memes is particularly challenging due to the combination of images, text, and emojis used to portray humor, satire, or sociopolitical commentary, as well as the low-resource nature of the Nepali language. We investigate a range of unimodal and multimodal modeling strategies, including text-only, vision-text, and caption-based approaches. For caption generation, the Gemini family of models (Gemini 2.X and Gemini 3.X) was used to produce contextually rich captions, which are publicly released as NeMeme-CAP on Hugging Face. Caption-based modeling leverages stochastic caption augmentation to address class imbalance and Test-Time Augmentation (TTA) to reduce prediction variance and improve model robustness. The best-performing system fine-tunes an encoder-only transformer model, RoBERTa-base, on the generated captions, achieving third place on the official leaderboard with a macro-averaged F1-score of 0.7397. The code is publicly available at https://github.com/thapaliya123/LREC-CHiPSAL-2026.

In multilingual contexts, an automated system for accurate language identification, followed by hate speech detection and target identification, plays a critical role in processing low-resource hate speech data and mitigating its negative impact. This paper presents our approach to the three subtasks in the Shared Task on Natural Language Understanding of Devanagari Script Languages at CHIPSAL@COLING 2025: (i) Language Identification, (ii) Hate Speech Detection, and (iii) Target Identification. Both classical machine learning and multilingual transformer models were explored, where MuRIL Large, trained on undersampled data for subtasks A and B outperformed the classical models. For subtask C, the Hybrid model trained on augmented data achieved superior performance over classical and transformer-based approaches. The top-performing models, named MURTweet for subtasks A and B and NER-MURTweet for subtask C, secured sixth, third, and first rank respectively, in the competition.