Large Vision-Language Models (VLMs) have achieved unprecedented success in several objective multimodal reasoning tasks. However, to further enhance their capabilities of empathetic and effective communication with humans, improving how VLMs process and understand emotions is crucial. Despite significant research attention on improving affective understanding, there is a lack of detailed evaluations of VLMs for emotion-related tasks, which can potentially help inform downstream fine-tuning efforts. In this work, we present the first comprehensive evaluation of VLMs for recognizing evoked emotions from images. We create a benchmark for the task of evoked emotion recognition and study the performance of VLMs for this task, from perspectives of correctness and robustness. Through several experiments, we demonstrate important factors that emotion recognition performance depends on, and also characterize the various errors made by VLMs in the process. Finally, we pinpoint potential causes for errors through a human evaluation study. We use our experimental results to inform recommendations for the future of emotion research in the context of VLMs.

WordNet serves as a very essential knowledge source for various downstream Natural Language Processing (NLP) tasks. Since this is a human-curated resource, building such a resource is very cumbersome and time-consuming. Even though for languages like English, the existing WordNet is reasonably rich in terms of coverage, for resource-poor languages like Bengali, the WordNet is far from being reasonably sufficient in terms of coverage of vocabulary and relations between them. In this paper, we investigate the usefulness of some of the existing knowledge graph completion algorithms to enrich Bengali WordNet automatically. We explore three such techniques namely DistMult, ComplEx, and HolE, and analyze their effectiveness for adding more relations between existing nodes in the WordNet. We achieve maximum Hits@1 of 0.412 and Hits@10 of 0.703, which look very promising for low resource languages like Bengali.