We describe the systems of the University of Alberta team for the
SemEval-2023 Visual Word Sense Disambiguation (V-WSD) Task. We present a novel
algorithm that leverages glosses retrieved from BabelNet, in combination with
text and image encoders. Furthermore, we compare language-specific encoders
against the application of English encoders to translated texts. As the
contexts given in the task datasets are extremely short, we also experiment
with augmenting these contexts with descriptions generated by a language model.
This yields substantial improvements in accuracy. We describe and evaluate
additional V-WSD methods which use image generation and text-conditioned image
segmentation. Overall, the results of our official submission rank us 18 out of
56 teams. Some of our unofficial results are even better than the official
ones. Our code is publicly available at this https URL

本文讲述了 Alberta 大学团队在 SemEval-2023 视觉词义消歧（V-WSD）任务中的系统，使用 BabelNet 中检索到的注释、文本和图像编码器的组合的新算法。此外，我们将语言特定的编码器与应用于翻译文本的英语编码器进行比较。由于任务数据集中提供的上下文非常短，因此我们还使用语言模型生成的描述来增强这些上下文，从而显著提高了准确性。我们还描述并评估了使用图像生成和文本条件图像分割的其他 V-WSD 方法。总体而言，我们的官方提交结果将我们排名第 18，一些我们的非官方结果甚至比官方结果更好。