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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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<body>
<div class="jumbotron jumbotron-fluid">
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<nav class="navbar navbar-expand-lg navbar-light bg-light">
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<li class="nav-item d-sm-none d-none d-md-none d-lg-block">
<a class="navbar-brand" href="https://unlp.org.ua/">
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<li class="nav-item">
<a class="nav-link" href="#about">About</a>
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<a class="nav-link" href="#results">Results</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>
</h6>
</div>
</div>
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<div class="section">
<div class="row justify-content-center">
<div class="col-sm-8">
<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>
</div>
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