@inproceedings{kazakov-etal-2023-webnlg,
title = "{W}eb{NLG}-Interno: Utilizing {FRED}-T5 to address the {RDF}-to-text problem ({W}eb{NLG} 2023)",
author = "Kazakov, Maxim and
Preobrazhenskaya, Julia and
Bulychev, Ivan and
Shain, Aleksandr",
editor = "Gatt, Albert and
Gardent, Claire and
Cripwell, Liam and
Belz, Anya and
Borg, Claudia and
Erdem, Aykut and
Erdem, Erkut",
booktitle = "Proceedings of the Workshop on Multimodal, Multilingual Natural Language Generation and Multilingual WebNLG Challenge (MM-NLG 2023)",
month = sep,
year = "2023",
address = "Prague, Czech Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.mmnlg-1.7",
pages = "67--72",
abstract = "We present our solution for the Russian RDF002 to-text generation task of the WebNLG Challenge 2023. We use the pretrained large language model named FRED-T5 (Zmitrovich et al., 2023) to finetune on the train dataset. Also, we propose several types of prompt and run experiments to analyze their effectiveness. Our submission achieves 0.373 TER on the test dataset, taking the first place according to the results of the automatic evaluation and outperforming the best result of the previous challenge by 0.025. The code of our solution is available at the following link: https://github.com/Ivan30003/webnlg{\_}interno",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kazakov-etal-2023-webnlg">
<titleInfo>
<title>WebNLG-Interno: Utilizing FRED-T5 to address the RDF-to-text problem (WebNLG 2023)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maxim</namePart>
<namePart type="family">Kazakov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Julia</namePart>
<namePart type="family">Preobrazhenskaya</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ivan</namePart>
<namePart type="family">Bulychev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aleksandr</namePart>
<namePart type="family">Shain</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-09</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Workshop on Multimodal, Multilingual Natural Language Generation and Multilingual WebNLG Challenge (MM-NLG 2023)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Albert</namePart>
<namePart type="family">Gatt</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Claire</namePart>
<namePart type="family">Gardent</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Liam</namePart>
<namePart type="family">Cripwell</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anya</namePart>
<namePart type="family">Belz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Claudia</namePart>
<namePart type="family">Borg</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aykut</namePart>
<namePart type="family">Erdem</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Erkut</namePart>
<namePart type="family">Erdem</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Prague, Czech Republic</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>We present our solution for the Russian RDF002 to-text generation task of the WebNLG Challenge 2023. We use the pretrained large language model named FRED-T5 (Zmitrovich et al., 2023) to finetune on the train dataset. Also, we propose several types of prompt and run experiments to analyze their effectiveness. Our submission achieves 0.373 TER on the test dataset, taking the first place according to the results of the automatic evaluation and outperforming the best result of the previous challenge by 0.025. The code of our solution is available at the following link: https://github.com/Ivan30003/webnlg_interno</abstract>
<identifier type="citekey">kazakov-etal-2023-webnlg</identifier>
<location>
<url>https://aclanthology.org/2023.mmnlg-1.7</url>
</location>
<part>
<date>2023-09</date>
<extent unit="page">
<start>67</start>
<end>72</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T WebNLG-Interno: Utilizing FRED-T5 to address the RDF-to-text problem (WebNLG 2023)
%A Kazakov, Maxim
%A Preobrazhenskaya, Julia
%A Bulychev, Ivan
%A Shain, Aleksandr
%Y Gatt, Albert
%Y Gardent, Claire
%Y Cripwell, Liam
%Y Belz, Anya
%Y Borg, Claudia
%Y Erdem, Aykut
%Y Erdem, Erkut
%S Proceedings of the Workshop on Multimodal, Multilingual Natural Language Generation and Multilingual WebNLG Challenge (MM-NLG 2023)
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czech Republic
%F kazakov-etal-2023-webnlg
%X We present our solution for the Russian RDF002 to-text generation task of the WebNLG Challenge 2023. We use the pretrained large language model named FRED-T5 (Zmitrovich et al., 2023) to finetune on the train dataset. Also, we propose several types of prompt and run experiments to analyze their effectiveness. Our submission achieves 0.373 TER on the test dataset, taking the first place according to the results of the automatic evaluation and outperforming the best result of the previous challenge by 0.025. The code of our solution is available at the following link: https://github.com/Ivan30003/webnlg_interno
%U https://aclanthology.org/2023.mmnlg-1.7
%P 67-72
Markdown (Informal)
[WebNLG-Interno: Utilizing FRED-T5 to address the RDF-to-text problem (WebNLG 2023)](https://aclanthology.org/2023.mmnlg-1.7) (Kazakov et al., MMNLG-WS 2023)
ACL