<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chen-gimpel-2020-learning">
    <titleInfo>
        <title>Learning Probabilistic Sentence Representations from Paraphrases</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Mingda</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kevin</namePart>
        <namePart type="family">Gimpel</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 5th Workshop on Representation Learning for NLP</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Spandana</namePart>
            <namePart type="family">Gella</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Johannes</namePart>
            <namePart type="family">Welbl</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Marek</namePart>
            <namePart type="family">Rei</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Fabio</namePart>
            <namePart type="family">Petroni</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Patrick</namePart>
            <namePart type="family">Lewis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Emma</namePart>
            <namePart type="family">Strubell</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Minjoon</namePart>
            <namePart type="family">Seo</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Hannaneh</namePart>
            <namePart type="family">Hajishirzi</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>Probabilistic word embeddings have shown effectiveness in capturing notions of generality and entailment, but there is very little work on doing the analogous type of investigation for sentences. In this paper we define probabilistic models that produce distributions for sentences. Our best-performing model treats each word as a linear transformation operator applied to a multivariate Gaussian distribution. We train our models on paraphrases and demonstrate that they naturally capture sentence specificity. While our proposed model achieves the best performance overall, we also show that specificity is represented by simpler architectures via the norm of the sentence vectors. Qualitative analysis shows that our probabilistic model captures sentential entailment and provides ways to analyze the specificity and preciseness of individual words.</abstract>
    <identifier type="citekey">chen-gimpel-2020-learning</identifier>
    <identifier type="doi">10.18653/v1/2020.repl4nlp-1.3</identifier>
    <location>
        <url>https://aclanthology.org/2020.repl4nlp-1.3/</url>
    </location>
    <part>
        <date>2020-07</date>
        <extent unit="page">
            <start>17</start>
            <end>23</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Learning Probabilistic Sentence Representations from Paraphrases
%A Chen, Mingda
%A Gimpel, Kevin
%Y Gella, Spandana
%Y Welbl, Johannes
%Y Rei, Marek
%Y Petroni, Fabio
%Y Lewis, Patrick
%Y Strubell, Emma
%Y Seo, Minjoon
%Y Hajishirzi, Hannaneh
%S Proceedings of the 5th Workshop on Representation Learning for NLP
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F chen-gimpel-2020-learning
%X Probabilistic word embeddings have shown effectiveness in capturing notions of generality and entailment, but there is very little work on doing the analogous type of investigation for sentences. In this paper we define probabilistic models that produce distributions for sentences. Our best-performing model treats each word as a linear transformation operator applied to a multivariate Gaussian distribution. We train our models on paraphrases and demonstrate that they naturally capture sentence specificity. While our proposed model achieves the best performance overall, we also show that specificity is represented by simpler architectures via the norm of the sentence vectors. Qualitative analysis shows that our probabilistic model captures sentential entailment and provides ways to analyze the specificity and preciseness of individual words.
%R 10.18653/v1/2020.repl4nlp-1.3
%U https://aclanthology.org/2020.repl4nlp-1.3/
%U https://doi.org/10.18653/v1/2020.repl4nlp-1.3
%P 17-23

Markdown (Informal)

[Learning Probabilistic Sentence Representations from Paraphrases](https://aclanthology.org/2020.repl4nlp-1.3/) (Chen & Gimpel, RepL4NLP 2020)

• Learning Probabilistic Sentence Representations from Paraphrases (Chen & Gimpel, RepL4NLP 2020)

ACL

• Mingda Chen and Kevin Gimpel. 2020. Learning Probabilistic Sentence Representations from Paraphrases. In Proceedings of the 5th Workshop on Representation Learning for NLP, pages 17–23, Online. Association for Computational Linguistics.