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        <title>Semantic Triples Verbalization with Generative Pre-Training Model</title>
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        <namePart type="given">Pavel</namePart>
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            <title>Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)</title>
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        <name type="personal">
            <namePart type="given">Thiago</namePart>
            <namePart type="family">Castro Ferreira</namePart>
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        <name type="personal">
            <namePart type="given">Claire</namePart>
            <namePart type="family">Gardent</namePart>
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        <name type="personal">
            <namePart type="given">Nikolai</namePart>
            <namePart type="family">Ilinykh</namePart>
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        <name type="personal">
            <namePart type="given">Chris</namePart>
            <namePart type="family">van der Lee</namePart>
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        <name type="personal">
            <namePart type="given">Simon</namePart>
            <namePart type="family">Mille</namePart>
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        <name type="personal">
            <namePart type="given">Diego</namePart>
            <namePart type="family">Moussallem</namePart>
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        <name type="personal">
            <namePart type="given">Anastasia</namePart>
            <namePart type="family">Shimorina</namePart>
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    <abstract>The paper devoted to the problem of automatic text generation from RDF triples. This problem was formalized and proposed as a part of the 2020 WebNLG challenge. We describe our approach to the RDF-to-text generation task based on a neural network model with the Generative Pre-Training (GPT-2) architecture. In particular, we outline a way of base GPT-2 model conversion to a model with language and classification heads and discuss the text generation methods. To research the parameters’ influence on the end-task performance a series of experiments was carried out. We report the result metrics and conclude with possible improvement directions.</abstract>
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%0 Conference Proceedings
%T Semantic Triples Verbalization with Generative Pre-Training Model
%A Blinov, Pavel
%Y Castro Ferreira, Thiago
%Y Gardent, Claire
%Y Ilinykh, Nikolai
%Y van der Lee, Chris
%Y Mille, Simon
%Y Moussallem, Diego
%Y Shimorina, Anastasia
%S Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)
%D 2020
%8 December
%I Association for Computational Linguistics
%C Dublin, Ireland (Virtual)
%F blinov-2020-semantic
%X The paper devoted to the problem of automatic text generation from RDF triples. This problem was formalized and proposed as a part of the 2020 WebNLG challenge. We describe our approach to the RDF-to-text generation task based on a neural network model with the Generative Pre-Training (GPT-2) architecture. In particular, we outline a way of base GPT-2 model conversion to a model with language and classification heads and discuss the text generation methods. To research the parameters’ influence on the end-task performance a series of experiments was carried out. We report the result metrics and conclude with possible improvement directions.
%U https://aclanthology.org/2020.webnlg-1.17
%P 154-158

Markdown (Informal)

[Semantic Triples Verbalization with Generative Pre-Training Model](https://aclanthology.org/2020.webnlg-1.17) (Blinov, WebNLG 2020)

• Semantic Triples Verbalization with Generative Pre-Training Model (Blinov, WebNLG 2020)

ACL

• Pavel Blinov. 2020. Semantic Triples Verbalization with Generative Pre-Training Model. In Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+), pages 154–158, Dublin, Ireland (Virtual). Association for Computational Linguistics.