Approximately 30% of autistic individuals remain non- or minimally-speaking throughout their lives, yet communicate richly through gestures, vocalizations, facial expressions, and augmentative devices. Interpreting this communication is an inherently multimodal task: caregivers rely on the simultaneous integration of visual cues, auditory signals, and contextual understanding to infer intent. Despite this natural alignment with multimodal large language models (MLLMs), research in this intersection remains narrowly focused on diagnosis rather than communication understanding. We address this gap by reframing the problem around two complementary dimensions: communicative actions (the physical modality) and communicative functions (the pragmatic intent). We analyze the ROSCO dataset, containing 2,903 caregiver-annotated video samples from 27 non- and minimally-speaking individuals, with multi-label annotations capturing up to three concurrent actions and two functions per sample across 6 action and 6 function classes. We further propose ROSCO-Omni, a teacher-student distillation framework that generates label-guided instruction data from a high-capability teacher MLLM and uses it to finetune a student MLLM for domain-specialized inference. ROSCO-Omni achieves performance comparable to closed-source models, demonstrating that open-source MLLMs can be adapted to understand communication in this underserved population.

Vaccination-related memes on social media play an increasingly influential role in shaping public perception of immunization, often spreading both supportive messaging and vaccine-critical narratives through multimodal communication. Detecting such content is challenging due to the combined use of images, embedded text, sarcasm, humor, and cultural references. This paper presents an overview of the Shared Task on Multimodal Identification of Vaccine Critical Content on Social Media, organized as part of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026) at ACL 2026. The task is based on the VaxMeme dataset, a large-scale collection of vaccination-related memes annotated into three classes: Vaccine-critical, Neutral, and Pro-vaccine. A total of 77 participants registered for the competition, with 25 teams submitting systems for evaluation. Participating approaches included transformer-based multimodal architectures, vision-language models, ensemble methods, and instruction-tuned large language models. The best-performing system achieved a macro F1-score of 0.8494. This shared task provides insights into the strengths and limitations of current multimodal approaches for vaccine stance detection and highlights future directions for robust public health misinformation analysis.

Online gaming communities are increasingly affected by toxic communication, including harassment, threats, hate speech, and extremist content. Detecting such behavior is challenging due to the short, noisy, multilingual, and highly imbalanced nature of gaming chat data. To advance research in this area, we organized the Shared Task on Fine-Grained Toxicity Detection in Online Gaming at EEUCA 2026, co-located with ACL 2026. The task is based on the GameTox dataset, containing approximately 53,000 annotated chat utterances from World of Tanks across six toxicity categories. A total of 102 participants took part, and 35 teams submitted systems exploring approaches such as domain-adaptive pretraining, multilingual transfer learning, contrastive learning, LLM-based augmentation, and ensemble methods. Systems were evaluated using macro-averaged F1-score, with the top system achieving 0.7041 Macro F1. This paper presents an overview of the shared task, dataset, evaluation framework, participant methods, and key findings.

Nonverbal vocalizations are an essential component of human communication, conveying rich information without linguistic content. However, their computational analysis is hindered by a lack of lexical anchors in the data, compounded by biased and imbalanced data distributions. While disentangled representation learning has shown promise in isolating specific speech features, its application to nonverbal vocalizations remains unexplored. In this paper, we introduce N-CORE, a novel backbone-agnostic framework designed to disentangle intertwined features like emotion and speaker information from nonverbal vocalizations by leveraging N views of audio samples to learn invariance to specific transformations. N-CORE achieves competitive performance compared to state-of-the-art methods for emotion and speaker classification on the VIVAE, ReCANVo, and ReCANVo-Balanced datasets. We further propose an emotion perturbation function that disrupts affective information while preserving speaker information in audio signals for emotion-invariant speaker classification. Our work informs research directions on paralinguistic speech processing, including clinical diagnoses of atypical speech and longitudinal analysis of communicative development. Our code is available at https://github.com/SiddhantBikram/N-CORE.

This paper presents the Shared Task on Multimodal Detection of Hate Speech, Humor, and Stance in Marginalized Socio-Political Movement Discourse, hosted at CASE 2025. The task is built on the PrideMM dataset, a curated collection of 5,063 text-embedded images related to the LGBTQ+ pride movement, annotated for four interrelated subtasks: (A) Hate Speech Detection, (B) Hate Target Classification, (C) Topical Stance Classification, and (D) Intended Humor Detection. Eighty-nine teams registered, with competitive submissions across all subtasks. The results show that multimodal approaches consistently outperform unimodal baselines, particularly for hate speech detection, while fine-grained tasks such as target identification and stance classification remain challenging due to label imbalance, multimodal ambiguity, and implicit or culturally specific content. CLIP-based models and parameter-efficient fusion architectures achieved strong performance, showing promising directions for low-resource and efficient multimodal systems.