Abstract
Visual Word Sense Disambiguation (V-WSD) identifies the correct visual sense of a multisense word in a specific context. This can be challenging as images may need to provide additional context and words may have multiple senses. A proper V-WSD system can benefit applications like image retrieval and captioning. This paper proposes a Prompt Generation approach to solve this challenge. This approach improves the robustness of language-image models like CLIP to contextual ambiguities and helps them better correlate between textual and visual contexts of different senses of words.
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CITATION STYLE
Ghahroodi, O., Dalili, S. A., Mesforoush, S., & Asgari, E. (2023). SUT at SemEval-2023 Task 1: Prompt Generation for Visual Word Sense Disambiguation. In 17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop (pp. 2160–2163). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.semeval-1.298
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