<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="akermi-etal-2020-tansformer">
    <titleInfo>
        <title>Transformer based Natural Language Generation for Question-Answering</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Imen</namePart>
        <namePart type="family">Akermi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Johannes</namePart>
        <namePart type="family">Heinecke</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Frédéric</namePart>
        <namePart type="family">Herledan</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>This paper explores Natural Language Generation within the context of Question-Answering task. The several works addressing this task only focused on generating a short answer or a long text span that contains the answer, while reasoning over a Web page or processing structured data. Such answers’ length are usually not appropriate as the answer tend to be perceived as too brief or too long to be read out loud by an intelligent assistant. In this work, we aim at generating a concise answer for a given question using an unsupervised approach that does not require annotated data. Tested over English and French datasets, the proposed approach shows very promising results.</abstract>
    <identifier type="citekey">akermi-etal-2020-tansformer</identifier>
    <identifier type="doi">10.18653/v1/2020.inlg-1.41</identifier>
    <location>
        <url>https://aclanthology.org/2020.inlg-1.41</url>
    </location>
    <part>
        <date>2020-12</date>
        <extent unit="page">
            <start>349</start>
            <end>359</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Transformer based Natural Language Generation for Question-Answering
%A Akermi, Imen
%A Heinecke, Johannes
%A Herledan, Frédéric
%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 akermi-etal-2020-tansformer
%X This paper explores Natural Language Generation within the context of Question-Answering task. The several works addressing this task only focused on generating a short answer or a long text span that contains the answer, while reasoning over a Web page or processing structured data. Such answers’ length are usually not appropriate as the answer tend to be perceived as too brief or too long to be read out loud by an intelligent assistant. In this work, we aim at generating a concise answer for a given question using an unsupervised approach that does not require annotated data. Tested over English and French datasets, the proposed approach shows very promising results.
%R 10.18653/v1/2020.inlg-1.41
%U https://aclanthology.org/2020.inlg-1.41
%U https://doi.org/10.18653/v1/2020.inlg-1.41
%P 349-359

Markdown (Informal)

[Transformer based Natural Language Generation for Question-Answering](https://aclanthology.org/2020.inlg-1.41) (Akermi et al., INLG 2020)

• Transformer based Natural Language Generation for Question-Answering (Akermi et al., INLG 2020)

ACL

• Imen Akermi, Johannes Heinecke, and Frédéric Herledan. 2020. Transformer based Natural Language Generation for Question-Answering. In Proceedings of the 13th International Conference on Natural Language Generation, pages 349–359, Dublin, Ireland. Association for Computational Linguistics.