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        <title>Extension Multi30K: Multimodal Dataset for Integrated Vision and Language Research in Ukrainian</title>
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    <name type="personal">
        <namePart type="given">Nataliia</namePart>
        <namePart type="family">Saichyshyna</namePart>
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    <name type="personal">
        <namePart type="given">Daniil</namePart>
        <namePart type="family">Maksymenko</namePart>
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        <namePart type="given">Oleksii</namePart>
        <namePart type="family">Turuta</namePart>
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    <name type="personal">
        <namePart type="given">Andriy</namePart>
        <namePart type="family">Yerokhin</namePart>
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        <namePart type="family">Babii</namePart>
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    <name type="personal">
        <namePart type="given">Olena</namePart>
        <namePart type="family">Turuta</namePart>
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            <title>Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)</title>
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            <namePart type="given">Mariana</namePart>
            <namePart type="family">Romanyshyn</namePart>
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    <abstract>We share the results of the project within the well-known Multi30k dataset dedicated to improving machine translation of text from English into Ukrainian. The main task was to manually prepare the dataset and improve the translation of texts. The importance of collecting such datasets for low-resource languages for improving the quality of machine translation has been discussed. We also studied the features of translations of words and sentences with ambiguous meanings. The collection of multimodal datasets is essential for natural language processing tasks because it allows the development of more complex and comprehensive machine learning models that can understand and analyze different types of data. These models can learn from a variety of data types, including images, text, and audio, for more accurate and meaningful results.</abstract>
    <identifier type="citekey">saichyshyna-etal-2023-extension</identifier>
    <identifier type="doi">10.18653/v1/2023.unlp-1.7</identifier>
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        <date>2023-05</date>
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            <start>54</start>
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%0 Conference Proceedings
%T Extension Multi30K: Multimodal Dataset for Integrated Vision and Language Research in Ukrainian
%A Saichyshyna, Nataliia
%A Maksymenko, Daniil
%A Turuta, Oleksii
%A Yerokhin, Andriy
%A Babii, Andrii
%A Turuta, Olena
%Y Romanyshyn, Mariana
%S Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F saichyshyna-etal-2023-extension
%X We share the results of the project within the well-known Multi30k dataset dedicated to improving machine translation of text from English into Ukrainian. The main task was to manually prepare the dataset and improve the translation of texts. The importance of collecting such datasets for low-resource languages for improving the quality of machine translation has been discussed. We also studied the features of translations of words and sentences with ambiguous meanings. The collection of multimodal datasets is essential for natural language processing tasks because it allows the development of more complex and comprehensive machine learning models that can understand and analyze different types of data. These models can learn from a variety of data types, including images, text, and audio, for more accurate and meaningful results.
%R 10.18653/v1/2023.unlp-1.7
%U https://aclanthology.org/2023.unlp-1.7
%U https://doi.org/10.18653/v1/2023.unlp-1.7
%P 54-61

Markdown (Informal)

[Extension Multi30K: Multimodal Dataset for Integrated Vision and Language Research in Ukrainian](https://aclanthology.org/2023.unlp-1.7) (Saichyshyna et al., UNLP 2023)

• Extension Multi30K: Multimodal Dataset for Integrated Vision and Language Research in Ukrainian (Saichyshyna et al., UNLP 2023)

ACL

• Nataliia Saichyshyna, Daniil Maksymenko, Oleksii Turuta, Andriy Yerokhin, Andrii Babii, and Olena Turuta. 2023. Extension Multi30K: Multimodal Dataset for Integrated Vision and Language Research in Ukrainian. In Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP), pages 54–61, Dubrovnik, Croatia. Association for Computational Linguistics.