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    <titleInfo>
        <title>Scalable Micro-planned Generation of Discourse from Structured Data</title>
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        <namePart type="given">Anirban</namePart>
        <namePart type="family">Laha</namePart>
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        <namePart type="given">Parag</namePart>
        <namePart type="family">Jain</namePart>
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    <name type="personal">
        <namePart type="given">Abhijit</namePart>
        <namePart type="family">Mishra</namePart>
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    <name type="personal">
        <namePart type="given">Karthik</namePart>
        <namePart type="family">Sankaranarayanan</namePart>
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    <abstract>We present a framework for generating natural language description from structured data such as tables; the problem comes under the category of data-to-text natural language generation (NLG). Modern data-to-text NLG systems typically use end-to-end statistical and neural architectures that learn from a limited amount of task-specific labeled data, and therefore exhibit limited scalability, domain-adaptability, and interpretability. Unlike these systems, ours is a modular, pipeline-based approach, and does not require task-specific parallel data. Rather, it relies on monolingual corpora and basic off-the-shelf NLP tools. This makes our system more scalable and easily adaptable to newer domains. Our system utilizes a three-staged pipeline that: (i) converts entries in the structured data to canonical form, (ii) generates simple sentences for each atomic entry in the canonicalized representation, and (iii) combines the sentences to produce a coherent, fluent, and adequate paragraph description through sentence compounding and co-reference replacement modules. Experiments on a benchmark mixed-domain data set curated for paragraph description from tables reveals the superiority of our system over existing data-to-text approaches. We also demonstrate the robustness of our system in accepting other popular data sets covering diverse data types such as knowledge graphs and key-value maps.</abstract>
    <identifier type="citekey">laha-etal-2019-scalable</identifier>
    <identifier type="doi">10.1162/coli_a_00363</identifier>
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        <date>2019-12</date>
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            <start>737</start>
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%0 Journal Article
%T Scalable Micro-planned Generation of Discourse from Structured Data
%A Laha, Anirban
%A Jain, Parag
%A Mishra, Abhijit
%A Sankaranarayanan, Karthik
%J Computational Linguistics
%D 2019
%8 December
%V 45
%N 4
%I MIT Press
%C Cambridge, MA
%F laha-etal-2019-scalable
%X We present a framework for generating natural language description from structured data such as tables; the problem comes under the category of data-to-text natural language generation (NLG). Modern data-to-text NLG systems typically use end-to-end statistical and neural architectures that learn from a limited amount of task-specific labeled data, and therefore exhibit limited scalability, domain-adaptability, and interpretability. Unlike these systems, ours is a modular, pipeline-based approach, and does not require task-specific parallel data. Rather, it relies on monolingual corpora and basic off-the-shelf NLP tools. This makes our system more scalable and easily adaptable to newer domains. Our system utilizes a three-staged pipeline that: (i) converts entries in the structured data to canonical form, (ii) generates simple sentences for each atomic entry in the canonicalized representation, and (iii) combines the sentences to produce a coherent, fluent, and adequate paragraph description through sentence compounding and co-reference replacement modules. Experiments on a benchmark mixed-domain data set curated for paragraph description from tables reveals the superiority of our system over existing data-to-text approaches. We also demonstrate the robustness of our system in accepting other popular data sets covering diverse data types such as knowledge graphs and key-value maps.
%R 10.1162/coli_a_00363
%U https://aclanthology.org/J19-4005/
%U https://doi.org/10.1162/coli_a_00363
%P 737-763

Markdown (Informal)

[Scalable Micro-planned Generation of Discourse from Structured Data](https://aclanthology.org/J19-4005/) (Laha et al., CL 2019)

• Scalable Micro-planned Generation of Discourse from Structured Data (Laha et al., CL 2019)

ACL

• Anirban Laha, Parag Jain, Abhijit Mishra, and Karthik Sankaranarayanan. 2019. Scalable Micro-planned Generation of Discourse from Structured Data. Computational Linguistics, 45(4):737–763.