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        <title>SzegedAI at SemEval-2023 Task 1: Applying Quasi-Symbolic Representations in Visual Word Sense Disambiguation</title>
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            <title>Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)</title>
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            <namePart type="given">Atul</namePart>
            <namePart type="given">Kr.</namePart>
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            <namePart type="given">Harish</namePart>
            <namePart type="family">Tayyar Madabushi</namePart>
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            <namePart type="given">Ritesh</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T SzegedAI at SemEval-2023 Task 1: Applying Quasi-Symbolic Representations in Visual Word Sense Disambiguation
%A Berend, Gábor
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F berend-2023-szegedai
%X 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.
%R 10.18653/v1/2023.semeval-1.270
%U https://aclanthology.org/2023.semeval-1.270/
%U https://doi.org/10.18653/v1/2023.semeval-1.270
%P 1965-1971

Markdown (Informal)

[SzegedAI at SemEval-2023 Task 1: Applying Quasi-Symbolic Representations in Visual Word Sense Disambiguation](https://aclanthology.org/2023.semeval-1.270/) (Berend, SemEval 2023)

• SzegedAI at SemEval-2023 Task 1: Applying Quasi-Symbolic Representations in Visual Word Sense Disambiguation (Berend, SemEval 2023)

ACL

• Gábor Berend. 2023. SzegedAI at SemEval-2023 Task 1: Applying Quasi-Symbolic Representations in Visual Word Sense Disambiguation. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1965–1971, Toronto, Canada. Association for Computational Linguistics.