Research on Event Extraction (EE) in South Asian languages is crucial for understanding information dissemination and enabling automated news analysis in morphologically complex, low-resource environments. To address the scarcity of high-quality, publicly available datasets, we present Nepali Event Extraction (NepEE), a manually annotated corpus comprising 10,226 Devanagari sentences. The dataset includes annotations for trigger spans and event types, achieving high inter-annotator agreement with Fleiss’ kappa = 0.812 for trigger identification and kappa = 0.855 for event classification. Our dataset was developed through a rigorous iterative three-phase protocol involving five expert native speakers to ensure linguistic precision. We conduct benchmarking across a broad spectrum of approaches, including classical feature-based models, five fine-tuned Transformer encoders, and contemporary instruction-tuned Large Language Models (LLMs) using zero-shot and fixed few-shot prompting. Our analysis shows that Indic-specialized Transformers achieve superior classification performance, while traditional methods and few-shot prompting struggle with the challenges of exact span extraction in morphologically complex contexts. Furthermore, we quantify performance differences between sentence-level and span-level tasks, providing strong baselines for future research. The findings and the released NepEE dataset provide a valuable resource for advancing event understanding in low-resource languages (LRLs). All code and resources are available at https://github.com/SUJAL390/EEUCA-ACL-2026-Trigger-Phrase-Identification-and-Event-Classification-in-Low-Resource-Languages.

The proliferation of health misinformation in Low-Resource Languages (LRLs) poses a severe threat to public health, yet automated detection remains critically under-studied due to the scarcity of high-quality benchmarks. We address this gap by introducing Nep-Health-Misinfo, a novel human-verified corpus for health misinformation identification in Nepali. The dataset was developed by adapting four foundational benchmarks (Monkeypox-V1, Monkeypox-V2, COVID-19, and CoAID) through a systematic Machine Translation Post-Editing (MTPE) protocol involving native experts. Our evaluation of Neural Machine Translation (NMT) systems reveals a significant translation asymmetry: while state-of-the-art (SOTA) systems achieve a BLEU score of 43.21 on factual health data, performance degrades sharply on deceptive narratives, with BLEU and TER scores dropping to 19.11 and 62.42, respectively. To establish robust baselines, we benchmark seven recent open-weight Large Language Models (LLMs), including Qwen2.5-7B-Instruct, Gemma-3-4B-IT, and Ministral-8B-Instruct, across zero-shot and few-shot settings. For the few-shot evaluation, we compare stochastic sampling against a K-means centroid-based approach for semantically representative exemplar selection. Experimental results indicate that Qwen2.5-7B-Instruct achieves a peak Macro F1-score of 0.8488, improving over its zero-shot performance (0.7188) on the same dataset. Our findings demonstrate that while few-shot prompting effectively mitigates distribution shifts in low-resource medical contexts, performance remains highly sensitive to the semantic density of exemplars. This work provides the first human-verified Nepali health misinformation corpus. All code and resources are available at https://github.com/SUJAL390/Nep-Health-Misinfo-CHIPSAL-LREC.

Memes, a multimodal form of communication, have emerged as a popular mode of expression in online discourse, particularly among marginalized groups. With multiple meanings, memes often combine satire, irony, and nuanced language, presenting particular challenges to machines in detecting hate speech, humor, stance, and the target of hostility. This paper presents a comparison of unimodal and multimodal solutions to address all four subtasks of the CASE 2025 Shared Task on Multimodal Hate, Humor, and Stance Detection. We compare transformer-based text models (BERT, RoBERTa) with CNN-based vision models (DenseNet, EfficientNet), and multimodal fusion methods, such as CLIP. We find that multimodal systems consistently outperform the unimodal baseline, with CLIP performing the best on all subtasks with a macro F1 score of 78% in sub-task A, 56% in sub-task B, 59% in sub-task C, and 72% in sub-task D.

Alzheimer’s Disease (AD) is a neurodegenerative disorder that affects cognitive abilities and memory, especially in older adults. One of the challenges of AD is that it can be difficult to diagnose in its early stages. However, recent research has shown that changes in language, including speech decline and difficulty in processing information, can be important indicators of AD and may help with early detection. Hence, the speech narratives of the patients can be useful in diagnosing the early stages of Alzheimer’s disease. While the previous works have presented the potential of using speech narratives to diagnose AD in high-resource languages, this work explores the possibility of using a low-resourced language, i.e., Hindi language, to diagnose AD. In this paper, we present a dataset specifically for analyzing AD in the Hindi language, along with experimental results using various state-of-the-art algorithms to assess the diagnostic potential of speech narratives in Hindi. Our analysis suggests that speech narratives in the Hindi language have the potential to aid in the diagnosis of AD. Our dataset and code are made publicly available at https://github.com/rkritesh210/DementiaBankHindi.