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<!DOCTYPE html> | ||
<html> | ||
<head> | ||
<meta charset="utf-8"> | ||
<meta name="viewport" content="width=device-width, initial-scale=1"> | ||
<title>Ukrainian Visual Word Sense Disambiguation Benchmark</title> | ||
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<a class="nav-link" href="#about">About</a> | ||
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<a class="nav-link" href="#results">Results</a> | ||
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<a class="nav-link" href="#bibtex">Bibtex</a> | ||
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<h2 class="text-center pt-5 pb-3">Ukrainian Visual Word Sense Disambiguation Benchmark</h2> | ||
<div class="row justify-content-center g-1"> | ||
<div class="col-md-2 text-center"> | ||
<a href="https://www.linkedin.com/in/yura-laba/" class="text-decoration-none" | ||
target="_blank"> | ||
<img src="img/yurii_laba.jpeg" | ||
class="rounded-circle img-fluid d-sm-none d-none d-md-block px-1"> | ||
<small>Yurii Laba</small> | ||
</a> | ||
</div> | ||
<div class="col-md-2 text-center"> | ||
<a href="https://apps.ucu.edu.ua/en/oles-dobosevych/" class="text-decoration-none" | ||
target="_blank"> | ||
<img src="img/oles_dobosevych.jpeg" | ||
class="rounded-circle img-fluid d-sm-none d-none d-md-block px-1"> | ||
<small>Oles Dobosevych</small> | ||
</a> | ||
</div> | ||
</div> | ||
<h6 class="text-center pt-3"> | ||
<a class="text-decoration-none" href="http://ucu.edu.ua/en" target="_blank">Ukrainian Catholic University</a> | ||
</h6> | ||
<h6 class="text-center"> | ||
<a class="btn btn-dark" href="http://arxiv.org/abs/xxxx.xxxx" role="button" target="_blank"> | ||
<span class="icon"><i class="ai ai-arxiv fa-fw"></i></span> | ||
<span>arXiv</span> | ||
</a> | ||
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<div class="section"> | ||
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<p>This study presents a benchmark for evaluating the Visual Word Sense Disambiguation | ||
(Visual-WSD) task in | ||
Ukrainian. The main goal of the Visual-WSD task is to identify, with minimal contextual | ||
information, the most | ||
appropriate representation of a given ambiguous word from a set of ten images. To construct | ||
this benchmark, we | ||
followed a methodology similar to that proposed by Raganato et al. (2023), who previously | ||
introduced benchmarks for | ||
the Visual-WSD task in English, Italian, and Farsi. This approach allows us to incorporate | ||
the Ukrainian benchmark | ||
into a broader framework for cross-language model performance comparisons. We collected the | ||
benchmark | ||
data semi-automatically and refined it with input from domain experts. We then assessed | ||
eight multilingual | ||
and multimodal language models using this benchmark. However, all tested models performed | ||
worse than the | ||
zero-shot CLIP-based baseline model (Radford et al., 2021) used by Raganato et al. (2023) | ||
for the English Visual- | ||
WSD task. Our analysis revealed a significant performance gap in the Visual-WSD task between | ||
Ukrainian and English.</p> | ||
</div> | ||
</div> | ||
</div> | ||
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