GPL at SemEval-2023 Task 1: WordNet and CLIP to disambiguate images

Given a word in context, the task of VisualWord Sense Disambiguation consists of selecting the correct image among a set of candidates. To select the correct image, we propose a solution blending text augmentation and multi-modal models. Text augmentation leverages the fine-grained semantic annotation from Word-Net to get a better representation of the textual component. We then compare this sense-augmented text to the image set using pre-trained multimodal models CLIP and ViLT. Our system has been ranked 16th for the English language, achieving 68.5 points for hit rate and 79.2 for mean reciprocal rank.