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        <title>Sequence-to-Sequence Models for Data-to-Text Natural Language Generation: Word- vs. Character-based Processing and Output Diversity</title>
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        <namePart type="given">Glorianna</namePart>
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        <namePart type="given">Sabrina</namePart>
        <namePart type="family">Jenne</namePart>
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    <name type="personal">
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        <namePart type="given">Thang</namePart>
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            <title>Proceedings of the 11th International Conference on Natural Language Generation</title>
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            <namePart type="given">Albert</namePart>
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    <abstract>We present a comparison of word-based and character-based sequence-to-sequence models for data-to-text natural language generation, which generate natural language descriptions for structured inputs. On the datasets of two recent generation challenges, our models achieve comparable or better automatic evaluation results than the best challenge submissions. Subsequent detailed statistical and human analyses shed light on the differences between the two input representations and the diversity of the generated texts. In a controlled experiment with synthetic training data generated from templates, we demonstrate the ability of neural models to learn novel combinations of the templates and thereby generalize beyond the linguistic structures they were trained on.</abstract>
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%0 Conference Proceedings
%T Sequence-to-Sequence Models for Data-to-Text Natural Language Generation: Word- vs. Character-based Processing and Output Diversity
%A Jagfeld, Glorianna
%A Jenne, Sabrina
%A Vu, Ngoc Thang
%Y Krahmer, Emiel
%Y Gatt, Albert
%Y Goudbeek, Martijn
%S Proceedings of the 11th International Conference on Natural Language Generation
%D 2018
%8 November
%I Association for Computational Linguistics
%C Tilburg University, The Netherlands
%F jagfeld-etal-2018-sequence
%X We present a comparison of word-based and character-based sequence-to-sequence models for data-to-text natural language generation, which generate natural language descriptions for structured inputs. On the datasets of two recent generation challenges, our models achieve comparable or better automatic evaluation results than the best challenge submissions. Subsequent detailed statistical and human analyses shed light on the differences between the two input representations and the diversity of the generated texts. In a controlled experiment with synthetic training data generated from templates, we demonstrate the ability of neural models to learn novel combinations of the templates and thereby generalize beyond the linguistic structures they were trained on.
%R 10.18653/v1/W18-6529
%U https://aclanthology.org/W18-6529
%U https://doi.org/10.18653/v1/W18-6529
%P 221-232

Markdown (Informal)

[Sequence-to-Sequence Models for Data-to-Text Natural Language Generation: Word- vs. Character-based Processing and Output Diversity](https://aclanthology.org/W18-6529) (Jagfeld et al., INLG 2018)

• Sequence-to-Sequence Models for Data-to-Text Natural Language Generation: Word- vs. Character-based Processing and Output Diversity (Jagfeld et al., INLG 2018)

ACL

• Glorianna Jagfeld, Sabrina Jenne, and Ngoc Thang Vu. 2018. Sequence-to-Sequence Models for Data-to-Text Natural Language Generation: Word- vs. Character-based Processing and Output Diversity. In Proceedings of the 11th International Conference on Natural Language Generation, pages 221–232, Tilburg University, The Netherlands. Association for Computational Linguistics.