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    <titleInfo>
        <title>Semi-Supervised Neural Text Generation by Joint Learning of Natural Language Generation and Natural Language Understanding Models</title>
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    <name type="personal">
        <namePart type="given">Raheel</namePart>
        <namePart type="family">Qader</namePart>
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    <name type="personal">
        <namePart type="given">François</namePart>
        <namePart type="family">Portet</namePart>
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    <name type="personal">
        <namePart type="given">Cyril</namePart>
        <namePart type="family">Labbé</namePart>
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        <dateIssued>2019-oct–nov</dateIssued>
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            <title>Proceedings of the 12th International Conference on Natural Language Generation</title>
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        <name type="personal">
            <namePart type="given">Kees</namePart>
            <namePart type="family">van Deemter</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Chenghua</namePart>
            <namePart type="family">Lin</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Hiroya</namePart>
            <namePart type="family">Takamura</namePart>
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    <abstract>In Natural Language Generation (NLG), End-to-End (E2E) systems trained through deep learning have recently gained a strong interest. Such deep models need a large amount of carefully annotated data to reach satisfactory performance. However, acquiring such datasets for every new NLG application is a tedious and time-consuming task. In this paper, we propose a semi-supervised deep learning scheme that can learn from non-annotated data and annotated data when available. It uses a NLG and a Natural Language Understanding (NLU) sequence-to-sequence models which are learned jointly to compensate for the lack of annotation. Experiments on two benchmark datasets show that, with limited amount of annotated data, the method can achieve very competitive results while not using any pre-processing or re-scoring tricks. These findings open the way to the exploitation of non-annotated datasets which is the current bottleneck for the E2E NLG system development to new applications.</abstract>
    <identifier type="citekey">qader-etal-2019-semi</identifier>
    <identifier type="doi">10.18653/v1/W19-8669</identifier>
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        <url>https://aclanthology.org/W19-8669</url>
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        <date>2019-oct–nov</date>
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            <start>552</start>
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%0 Conference Proceedings
%T Semi-Supervised Neural Text Generation by Joint Learning of Natural Language Generation and Natural Language Understanding Models
%A Qader, Raheel
%A Portet, François
%A Labbé, Cyril
%Y van Deemter, Kees
%Y Lin, Chenghua
%Y Takamura, Hiroya
%S Proceedings of the 12th International Conference on Natural Language Generation
%D 2019
%8 oct–nov
%I Association for Computational Linguistics
%C Tokyo, Japan
%F qader-etal-2019-semi
%X In Natural Language Generation (NLG), End-to-End (E2E) systems trained through deep learning have recently gained a strong interest. Such deep models need a large amount of carefully annotated data to reach satisfactory performance. However, acquiring such datasets for every new NLG application is a tedious and time-consuming task. In this paper, we propose a semi-supervised deep learning scheme that can learn from non-annotated data and annotated data when available. It uses a NLG and a Natural Language Understanding (NLU) sequence-to-sequence models which are learned jointly to compensate for the lack of annotation. Experiments on two benchmark datasets show that, with limited amount of annotated data, the method can achieve very competitive results while not using any pre-processing or re-scoring tricks. These findings open the way to the exploitation of non-annotated datasets which is the current bottleneck for the E2E NLG system development to new applications.
%R 10.18653/v1/W19-8669
%U https://aclanthology.org/W19-8669
%U https://doi.org/10.18653/v1/W19-8669
%P 552-562

Markdown (Informal)

[Semi-Supervised Neural Text Generation by Joint Learning of Natural Language Generation and Natural Language Understanding Models](https://aclanthology.org/W19-8669) (Qader et al., INLG 2019)

• Semi-Supervised Neural Text Generation by Joint Learning of Natural Language Generation and Natural Language Understanding Models (Qader et al., INLG 2019)

ACL

• Raheel Qader, François Portet, and Cyril Labbé. 2019. Semi-Supervised Neural Text Generation by Joint Learning of Natural Language Generation and Natural Language Understanding Models. In Proceedings of the 12th International Conference on Natural Language Generation, pages 552–562, Tokyo, Japan. Association for Computational Linguistics.