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- W4385571284 abstract "In this paper, we introduce our submission in the task of visual word sense disambiguation (vWSD). Our proposed solution operates by deriving quasi-symbolic semantic categories from the hidden representations of multi-modal text-image encoders. Our results are mixed, as we manage to achieve a substantial boost in performance when evaluating on a validation set, however, we experienced detrimental effects during evaluation on the actual test set. Our positive results on the validation set confirms the validity of the quasi-symbolic features, whereas our results on the test set revealed that the proposed technique was not able to cope with the sufficiently different distribution of the test data." @default.
- W4385571284 created "2023-08-05" @default.
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- W4385571284 date "2023-01-01" @default.
- W4385571284 modified "2023-09-24" @default.
- W4385571284 title "SzegedAI at SemEval-2023 Task 1: Applying Quasi-Symbolic Representations in Visual Word Sense Disambiguation" @default.
- W4385571284 doi "https://doi.org/10.18653/v1/2023.semeval-1.270" @default.
- W4385571284 hasPublicationYear "2023" @default.
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