<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="huang-etal-2020-inset">
    <titleInfo>
        <title>INSET: Sentence Infilling with INter-SEntential Transformer</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yichen</namePart>
        <namePart type="family">Huang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yizhe</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Oussama</namePart>
        <namePart type="family">Elachqar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yu</namePart>
        <namePart type="family">Cheng</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joyce</namePart>
            <namePart type="family">Chai</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Natalie</namePart>
            <namePart type="family">Schluter</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joel</namePart>
            <namePart type="family">Tetreault</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Missing sentence generation (or sentence in-filling) fosters a wide range of applications in natural language generation, such as document auto-completion and meeting note expansion. This task asks the model to generate intermediate missing sentences that can syntactically and semantically bridge the surrounding context. Solving the sentence infilling task requires techniques in natural language processing ranging from understanding to discourse-level planning to generation. In this paper, we propose a framework to decouple the challenge and address these three aspects respectively, leveraging the power of existing large-scale pre-trained models such as BERT and GPT-2. We empirically demonstrate the effectiveness of our model in learning a sentence representation for generation and further generating a missing sentence that fits the context.</abstract>
    <identifier type="citekey">huang-etal-2020-inset</identifier>
    <identifier type="doi">10.18653/v1/2020.acl-main.226</identifier>
    <location>
        <url>https://aclanthology.org/2020.acl-main.226</url>
    </location>
    <part>
        <date>2020-07</date>
        <extent unit="page">
            <start>2502</start>
            <end>2515</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T INSET: Sentence Infilling with INter-SEntential Transformer
%A Huang, Yichen
%A Zhang, Yizhe
%A Elachqar, Oussama
%A Cheng, Yu
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F huang-etal-2020-inset
%X Missing sentence generation (or sentence in-filling) fosters a wide range of applications in natural language generation, such as document auto-completion and meeting note expansion. This task asks the model to generate intermediate missing sentences that can syntactically and semantically bridge the surrounding context. Solving the sentence infilling task requires techniques in natural language processing ranging from understanding to discourse-level planning to generation. In this paper, we propose a framework to decouple the challenge and address these three aspects respectively, leveraging the power of existing large-scale pre-trained models such as BERT and GPT-2. We empirically demonstrate the effectiveness of our model in learning a sentence representation for generation and further generating a missing sentence that fits the context.
%R 10.18653/v1/2020.acl-main.226
%U https://aclanthology.org/2020.acl-main.226
%U https://doi.org/10.18653/v1/2020.acl-main.226
%P 2502-2515

Markdown (Informal)

[INSET: Sentence Infilling with INter-SEntential Transformer](https://aclanthology.org/2020.acl-main.226) (Huang et al., ACL 2020)

• INSET: Sentence Infilling with INter-SEntential Transformer (Huang et al., ACL 2020)

ACL

• Yichen Huang, Yizhe Zhang, Oussama Elachqar, and Yu Cheng. 2020. INSET: Sentence Infilling with INter-SEntential Transformer. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2502–2515, Online. Association for Computational Linguistics.