<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="bien-etal-2020-recipenlg">
    <titleInfo>
        <title>RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Michał</namePart>
        <namePart type="family">Bień</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Michał</namePart>
        <namePart type="family">Gilski</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Martyna</namePart>
        <namePart type="family">Maciejewska</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Wojciech</namePart>
        <namePart type="family">Taisner</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Dawid</namePart>
        <namePart type="family">Wisniewski</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Agnieszka</namePart>
        <namePart type="family">Lawrynowicz</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 13th International Conference on Natural Language Generation</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Brian</namePart>
            <namePart type="family">Davis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yvette</namePart>
            <namePart type="family">Graham</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">John</namePart>
            <namePart type="family">Kelleher</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yaji</namePart>
            <namePart type="family">Sripada</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Dublin, Ireland</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Semi-structured text generation is a non-trivial problem. Although last years have brought lots of improvements in natural language generation, thanks to the development of neural models trained on large scale datasets, these approaches still struggle with producing structured, context- and commonsense-aware texts. Moreover, it is not clear how to evaluate the quality of generated texts. To address these problems, we introduce RecipeNLG – a novel dataset of cooking recipes. We discuss the data collection process and the relation between the semi-structured texts and cooking recipes. We use the dataset to approach the problem of generating recipes. Finally, we make use of multiple metrics to evaluate the generated recipes.</abstract>
    <identifier type="citekey">bien-etal-2020-recipenlg</identifier>
    <identifier type="doi">10.18653/v1/2020.inlg-1.4</identifier>
    <location>
        <url>https://aclanthology.org/2020.inlg-1.4/</url>
    </location>
    <part>
        <date>2020-12</date>
        <extent unit="page">
            <start>22</start>
            <end>28</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation
%A Bień, Michał
%A Gilski, Michał
%A Maciejewska, Martyna
%A Taisner, Wojciech
%A Wisniewski, Dawid
%A Lawrynowicz, Agnieszka
%Y Davis, Brian
%Y Graham, Yvette
%Y Kelleher, John
%Y Sripada, Yaji
%S Proceedings of the 13th International Conference on Natural Language Generation
%D 2020
%8 December
%I Association for Computational Linguistics
%C Dublin, Ireland
%F bien-etal-2020-recipenlg
%X Semi-structured text generation is a non-trivial problem. Although last years have brought lots of improvements in natural language generation, thanks to the development of neural models trained on large scale datasets, these approaches still struggle with producing structured, context- and commonsense-aware texts. Moreover, it is not clear how to evaluate the quality of generated texts. To address these problems, we introduce RecipeNLG – a novel dataset of cooking recipes. We discuss the data collection process and the relation between the semi-structured texts and cooking recipes. We use the dataset to approach the problem of generating recipes. Finally, we make use of multiple metrics to evaluate the generated recipes.
%R 10.18653/v1/2020.inlg-1.4
%U https://aclanthology.org/2020.inlg-1.4/
%U https://doi.org/10.18653/v1/2020.inlg-1.4
%P 22-28

Markdown (Informal)

[RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation](https://aclanthology.org/2020.inlg-1.4/) (Bień et al., INLG 2020)

• RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation (Bień et al., INLG 2020)

ACL

• Michał Bień, Michał Gilski, Martyna Maciejewska, Wojciech Taisner, Dawid Wisniewski, and Agnieszka Lawrynowicz. 2020. RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation. In Proceedings of the 13th International Conference on Natural Language Generation, pages 22–28, Dublin, Ireland. Association for Computational Linguistics.