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We describe the systems of the University of Alberta team for theSemEval-2023 Visual Word Sense Disambiguation (V-WSD) Task. We present a novelalgorithm that leverages glosses retrieved from BabelNet, in combination withtext and image encoders. Furthermore, we compare language-specific encodersagainst the application of English encoders to translated texts. As thecontexts given in the task datasets are extremely short, we also experimentwith augmenting these contexts with descriptions generated by a language model.This yields substantial improvements in accuracy. We describe and evaluateadditional V-WSD methods which use image generation and text-conditioned imagesegmentation. Overall, the results of our official submission rank us 18 out of56 teams. Some of our unofficial results are even better than the officialones. Our code is publicly available at https://github.com/UAlberta-NLP/v-wsd.

UAlberta at SemEval-2023 Task 1: Context Augmentation and Translation for Multilingual Visual Word Sense Disambiguation