@inproceedings{poth-etal-2023-ml,
title = "{ML} Mob at {S}em{E}val-2023 Task 1: Probing {CLIP} on Visual Word-Sense Disambiguation",
author = "Poth, Clifton and
Hentschel, Martin and
Werner, Tobias and
Sterz, Hannah and
Bongard, Leonard",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.201",
doi = "10.18653/v1/2023.semeval-1.201",
pages = "1463--1469",
abstract = "Successful word sense disambiguation (WSD)is a fundamental element of natural languageunderstanding. As part of SemEval-2023 Task1, we investigate WSD in a multimodal setting,where ambiguous words are to be matched withcandidate images representing word senses. Wecompare multiple systems based on pre-trainedCLIP models. In our experiments, we findCLIP to have solid zero-shot performance onmonolingual and multilingual data. By em-ploying different fine-tuning techniques, we areable to further enhance performance. However,transferring knowledge between data distribu-tions proves to be more challenging.",
}
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<abstract>Successful word sense disambiguation (WSD)is a fundamental element of natural languageunderstanding. As part of SemEval-2023 Task1, we investigate WSD in a multimodal setting,where ambiguous words are to be matched withcandidate images representing word senses. Wecompare multiple systems based on pre-trainedCLIP models. In our experiments, we findCLIP to have solid zero-shot performance onmonolingual and multilingual data. By em-ploying different fine-tuning techniques, we areable to further enhance performance. However,transferring knowledge between data distribu-tions proves to be more challenging.</abstract>
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%0 Conference Proceedings
%T ML Mob at SemEval-2023 Task 1: Probing CLIP on Visual Word-Sense Disambiguation
%A Poth, Clifton
%A Hentschel, Martin
%A Werner, Tobias
%A Sterz, Hannah
%A Bongard, Leonard
%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 poth-etal-2023-ml
%X Successful word sense disambiguation (WSD)is a fundamental element of natural languageunderstanding. As part of SemEval-2023 Task1, we investigate WSD in a multimodal setting,where ambiguous words are to be matched withcandidate images representing word senses. Wecompare multiple systems based on pre-trainedCLIP models. In our experiments, we findCLIP to have solid zero-shot performance onmonolingual and multilingual data. By em-ploying different fine-tuning techniques, we areable to further enhance performance. However,transferring knowledge between data distribu-tions proves to be more challenging.
%R 10.18653/v1/2023.semeval-1.201
%U https://aclanthology.org/2023.semeval-1.201
%U https://doi.org/10.18653/v1/2023.semeval-1.201
%P 1463-1469
Markdown (Informal)
[ML Mob at SemEval-2023 Task 1: Probing CLIP on Visual Word-Sense Disambiguation](https://aclanthology.org/2023.semeval-1.201) (Poth et al., SemEval 2023)
ACL