<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ponti-etal-2017-decoding">
    <titleInfo>
        <title>Decoding Sentiment from Distributed Representations of Sentences</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Edoardo</namePart>
        <namePart type="given">Maria</namePart>
        <namePart type="family">Ponti</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ivan</namePart>
        <namePart type="family">Vulić</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Anna</namePart>
        <namePart type="family">Korhonen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Nancy</namePart>
            <namePart type="family">Ide</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Aurélie</namePart>
            <namePart type="family">Herbelot</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lluís</namePart>
            <namePart type="family">Màrquez</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Vancouver, Canada</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Distributed representations of sentences have been developed recently to represent their meaning as real-valued vectors. However, it is not clear how much information such representations retain about the polarity of sentences. To study this question, we decode sentiment from unsupervised sentence representations learned with different architectures (sensitive to the order of words, the order of sentences, or none) in 9 typologically diverse languages. Sentiment results from the (recursive) composition of lexical items and grammatical strategies such as negation and concession. The results are manifold: we show that there is no ‘one-size-fits-all’ representation architecture outperforming the others across the board. Rather, the top-ranking architectures depend on the language at hand. Moreover, we find that in several cases the additive composition model based on skip-gram word vectors may surpass supervised state-of-art architectures such as bi-directional LSTMs. Finally, we provide a possible explanation of the observed variation based on the type of negative constructions in each language.</abstract>
    <identifier type="citekey">ponti-etal-2017-decoding</identifier>
    <identifier type="doi">10.18653/v1/S17-1003</identifier>
    <location>
        <url>https://aclanthology.org/S17-1003</url>
    </location>
    <part>
        <date>2017-08</date>
        <extent unit="page">
            <start>22</start>
            <end>32</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Decoding Sentiment from Distributed Representations of Sentences
%A Ponti, Edoardo Maria
%A Vulić, Ivan
%A Korhonen, Anna
%Y Ide, Nancy
%Y Herbelot, Aurélie
%Y Màrquez, Lluís
%S Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F ponti-etal-2017-decoding
%X Distributed representations of sentences have been developed recently to represent their meaning as real-valued vectors. However, it is not clear how much information such representations retain about the polarity of sentences. To study this question, we decode sentiment from unsupervised sentence representations learned with different architectures (sensitive to the order of words, the order of sentences, or none) in 9 typologically diverse languages. Sentiment results from the (recursive) composition of lexical items and grammatical strategies such as negation and concession. The results are manifold: we show that there is no ‘one-size-fits-all’ representation architecture outperforming the others across the board. Rather, the top-ranking architectures depend on the language at hand. Moreover, we find that in several cases the additive composition model based on skip-gram word vectors may surpass supervised state-of-art architectures such as bi-directional LSTMs. Finally, we provide a possible explanation of the observed variation based on the type of negative constructions in each language.
%R 10.18653/v1/S17-1003
%U https://aclanthology.org/S17-1003
%U https://doi.org/10.18653/v1/S17-1003
%P 22-32

Markdown (Informal)

[Decoding Sentiment from Distributed Representations of Sentences](https://aclanthology.org/S17-1003) (Ponti et al., *SEM 2017)

• Decoding Sentiment from Distributed Representations of Sentences (Ponti et al., *SEM 2017)

ACL

• Edoardo Maria Ponti, Ivan Vulić, and Anna Korhonen. 2017. Decoding Sentiment from Distributed Representations of Sentences. In Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017), pages 22–32, Vancouver, Canada. Association for Computational Linguistics.