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        <title>RDFjsRealB: a Symbolic Approach for Generating Text from RDF Triples</title>
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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">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>This paper describes the Resource Description Framework (RDF) triples verbalizer developed for the WEB NLG CHALLENGE 2020 shared task. After reviewing representative works in Natural Language Generation in the context of the Semantic Web, the task is then described. We then sketch the symbolic approach we used for verbalizing RDF triples: once the triples are grouped by subject, each group is realized as one or more sentences using templates written in Python whose output is feed to an English realizer written in Javascript. The system was developed using the test data of the previous edition of the task and the train and development data of this year’s task. The automatic scores for this year’s test data are quite competitive. We conclude with a critical review of the data and discuss the suitability of this competition results in a wider Natural Language Generation setting.</abstract>
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%0 Conference Proceedings
%T RDFjsRealB: a Symbolic Approach for Generating Text from RDF Triples
%A Lapalme, Guy
%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 lapalme-2020-rdfjsrealb
%X This paper describes the Resource Description Framework (RDF) triples verbalizer developed for the WEB NLG CHALLENGE 2020 shared task. After reviewing representative works in Natural Language Generation in the context of the Semantic Web, the task is then described. We then sketch the symbolic approach we used for verbalizing RDF triples: once the triples are grouped by subject, each group is realized as one or more sentences using templates written in Python whose output is feed to an English realizer written in Javascript. The system was developed using the test data of the previous edition of the task and the train and development data of this year’s task. The automatic scores for this year’s test data are quite competitive. We conclude with a critical review of the data and discuss the suitability of this competition results in a wider Natural Language Generation setting.
%U https://aclanthology.org/2020.webnlg-1.16
%P 144-153

Markdown (Informal)

[RDFjsRealB: a Symbolic Approach for Generating Text from RDF Triples](https://aclanthology.org/2020.webnlg-1.16) (Lapalme, WebNLG 2020)

• RDFjsRealB: a Symbolic Approach for Generating Text from RDF Triples (Lapalme, WebNLG 2020)

ACL

• Guy Lapalme. 2020. RDFjsRealB: a Symbolic Approach for Generating Text from RDF Triples. In Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+), pages 144–153, Dublin, Ireland (Virtual). Association for Computational Linguistics.