Visual Word Sense Disambiguation (VWSD) is a task to find the image that most accurately depicts the correct sense of the target word for the given context. Previously, image-text matching models often suffered from recognizing polysemous words. This paper introduces an unsupervised VWSD approach that uses gloss information of an external lexical knowledge-base, especially the sense definitions. Specifically, we suggest employing Bayesian inference to incorporate the sense definitions when sense information of the answer is not provided. In addition, to ameliorate the out-of-dictionary (OOD) issue, we propose a context-aware definition generation with GPT-3. Experimental results show that the VWSD performance significantly increased with our Bayesian inference-based approach. In addition, our context-aware definition generation achieved prominent performance improvement in OOD examples exhibiting better performance than the existing definition generation method. We will publish source codes as soon as possible.

翻译：视觉词义消歧（VWSD）是一项任务，旨在寻找最能准确描述目标词在给定上下文中正确语义的图像。以往，图文匹配模型在处理多义词时常常表现不佳。本文提出了一种无监督的VWSD方法，利用外部词汇知识库中的释义信息，尤其是词义的定义。具体而言，我们建议在答案的语义信息未提供时，采用贝叶斯推理来融合这些定义。此外，为了缓解词典外（OOD）问题，我们提出了一种基于GPT-3的上下文感知定义生成方法。实验结果表明，基于贝叶斯推理的方法显著提升了VWSD的性能。同时，我们的上下文感知定义生成方法在OOD示例上取得了显著性能提升，优于现有的定义生成方法。我们将尽快发布源代码。