<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="huber-etal-2020-sentence">
    <titleInfo>
        <title>Do sentence embeddings capture discourse properties of sentences from Scientific Abstracts ?</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Laurine</namePart>
        <namePart type="family">Huber</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Chaker</namePart>
        <namePart type="family">Memmadi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mathilde</namePart>
        <namePart type="family">Dargnat</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yannick</namePart>
        <namePart type="family">Toussaint</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the First Workshop on Computational Approaches to Discourse</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Chloé</namePart>
            <namePart type="family">Braud</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Christian</namePart>
            <namePart type="family">Hardmeier</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Junyi</namePart>
            <namePart type="given">Jessy</namePart>
            <namePart type="family">Li</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Annie</namePart>
            <namePart type="family">Louis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Michael</namePart>
            <namePart type="family">Strube</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>We introduce four tasks designed to determine which sentence encoders best capture discourse properties of sentences from scientific abstracts, namely coherence and cohesion between clauses of a sentence, and discourse relations within sentences. We show that even if contextual encoders such as BERT or SciBERT encodes the coherence in discourse units, they do not help to predict three discourse relations commonly used in scientific abstracts. We discuss what these results underline, namely that these discourse relations are based on particular phrasing that allow non-contextual encoders to perform well.</abstract>
    <identifier type="citekey">huber-etal-2020-sentence</identifier>
    <identifier type="doi">10.18653/v1/2020.codi-1.9</identifier>
    <location>
        <url>https://aclanthology.org/2020.codi-1.9</url>
    </location>
    <part>
        <date>2020-11</date>
        <extent unit="page">
            <start>86</start>
            <end>95</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Do sentence embeddings capture discourse properties of sentences from Scientific Abstracts ?
%A Huber, Laurine
%A Memmadi, Chaker
%A Dargnat, Mathilde
%A Toussaint, Yannick
%Y Braud, Chloé
%Y Hardmeier, Christian
%Y Li, Junyi Jessy
%Y Louis, Annie
%Y Strube, Michael
%S Proceedings of the First Workshop on Computational Approaches to Discourse
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F huber-etal-2020-sentence
%X We introduce four tasks designed to determine which sentence encoders best capture discourse properties of sentences from scientific abstracts, namely coherence and cohesion between clauses of a sentence, and discourse relations within sentences. We show that even if contextual encoders such as BERT or SciBERT encodes the coherence in discourse units, they do not help to predict three discourse relations commonly used in scientific abstracts. We discuss what these results underline, namely that these discourse relations are based on particular phrasing that allow non-contextual encoders to perform well.
%R 10.18653/v1/2020.codi-1.9
%U https://aclanthology.org/2020.codi-1.9
%U https://doi.org/10.18653/v1/2020.codi-1.9
%P 86-95

Markdown (Informal)

[Do sentence embeddings capture discourse properties of sentences from Scientific Abstracts ?](https://aclanthology.org/2020.codi-1.9) (Huber et al., CODI 2020)

• Do sentence embeddings capture discourse properties of sentences from Scientific Abstracts ? (Huber et al., CODI 2020)

ACL

• Laurine Huber, Chaker Memmadi, Mathilde Dargnat, and Yannick Toussaint. 2020. Do sentence embeddings capture discourse properties of sentences from Scientific Abstracts ?. In Proceedings of the First Workshop on Computational Approaches to Discourse, pages 86–95, Online. Association for Computational Linguistics.