<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="jawanpuria-etal-2019-learning">
    <titleInfo>
        <title>Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Pratik</namePart>
        <namePart type="family">Jawanpuria</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Arjun</namePart>
        <namePart type="family">Balgovind</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Anoop</namePart>
        <namePart type="family">Kunchukuttan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Bamdev</namePart>
        <namePart type="family">Mishra</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <genre authority="bibutilsgt">journal article</genre>
    <relatedItem type="host">
        <titleInfo>
            <title>Transactions of the Association for Computational Linguistics</title>
        </titleInfo>
        <originInfo>
            <issuance>continuing</issuance>
            <publisher>MIT Press</publisher>
            <place>
                <placeTerm type="text">Cambridge, MA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">periodical</genre>
        <genre authority="bibutilsgt">academic journal</genre>
    </relatedItem>
    <abstract>We propose a novel geometric approach for learning bilingual mappings given monolingual embeddings and a bilingual dictionary. Our approach decouples the source-to-target language transformation into (a) language-specific rotations on the original embeddings to align them in a common, latent space, and (b) a language-independent similarity metric in this common space to better model the similarity between the embeddings. Overall, we pose the bilingual mapping problem as a classification problem on smooth Riemannian manifolds. Empirically, our approach outperforms previous approaches on the bilingual lexicon induction and cross-lingual word similarity tasks. We next generalize our framework to represent multiple languages in a common latent space. Language-specific rotations for all the languages and a common similarity metric in the latent space are learned jointly from bilingual dictionaries for multiple language pairs. We illustrate the effectiveness of joint learning for multiple languages in an indirect word translation setting.</abstract>
    <identifier type="citekey">jawanpuria-etal-2019-learning</identifier>
    <identifier type="doi">10.1162/tacl_a_00257</identifier>
    <location>
        <url>https://aclanthology.org/Q19-1007/</url>
    </location>
    <part>
        <date>2019</date>
        <detail type="volume"><number>7</number></detail>
        <extent unit="page">
            <start>107</start>
            <end>120</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Journal Article
%T Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach
%A Jawanpuria, Pratik
%A Balgovind, Arjun
%A Kunchukuttan, Anoop
%A Mishra, Bamdev
%J Transactions of the Association for Computational Linguistics
%D 2019
%V 7
%I MIT Press
%C Cambridge, MA
%F jawanpuria-etal-2019-learning
%X We propose a novel geometric approach for learning bilingual mappings given monolingual embeddings and a bilingual dictionary. Our approach decouples the source-to-target language transformation into (a) language-specific rotations on the original embeddings to align them in a common, latent space, and (b) a language-independent similarity metric in this common space to better model the similarity between the embeddings. Overall, we pose the bilingual mapping problem as a classification problem on smooth Riemannian manifolds. Empirically, our approach outperforms previous approaches on the bilingual lexicon induction and cross-lingual word similarity tasks. We next generalize our framework to represent multiple languages in a common latent space. Language-specific rotations for all the languages and a common similarity metric in the latent space are learned jointly from bilingual dictionaries for multiple language pairs. We illustrate the effectiveness of joint learning for multiple languages in an indirect word translation setting.
%R 10.1162/tacl_a_00257
%U https://aclanthology.org/Q19-1007/
%U https://doi.org/10.1162/tacl_a_00257
%P 107-120

Markdown (Informal)

[Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach](https://aclanthology.org/Q19-1007/) (Jawanpuria et al., TACL 2019)

• Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach (Jawanpuria et al., TACL 2019)

ACL

• Pratik Jawanpuria, Arjun Balgovind, Anoop Kunchukuttan, and Bamdev Mishra. 2019. Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach. Transactions of the Association for Computational Linguistics, 7:107–120.