<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ri-tsuruoka-2020-revisiting">
    <titleInfo>
        <title>Revisiting the Context Window for Cross-lingual Word Embeddings</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ryokan</namePart>
        <namePart type="family">Ri</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yoshimasa</namePart>
        <namePart type="family">Tsuruoka</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>Existing approaches to mapping-based cross-lingual word embeddings are based on the assumption that the source and target embedding spaces are structurally similar. The structures of embedding spaces largely depend on the co-occurrence statistics of each word, which the choice of context window determines. Despite this obvious connection between the context window and mapping-based cross-lingual embeddings, their relationship has been underexplored in prior work. In this work, we provide a thorough evaluation, in various languages, domains, and tasks, of bilingual embeddings trained with different context windows. The highlight of our findings is that increasing the size of both the source and target window sizes improves the performance of bilingual lexicon induction, especially the performance on frequent nouns.</abstract>
    <identifier type="citekey">ri-tsuruoka-2020-revisiting</identifier>
    <identifier type="doi">10.18653/v1/2020.acl-main.94</identifier>
    <location>
        <url>https://aclanthology.org/2020.acl-main.94</url>
    </location>
    <part>
        <date>2020-07</date>
        <extent unit="page">
            <start>995</start>
            <end>1005</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Revisiting the Context Window for Cross-lingual Word Embeddings
%A Ri, Ryokan
%A Tsuruoka, Yoshimasa
%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 ri-tsuruoka-2020-revisiting
%X Existing approaches to mapping-based cross-lingual word embeddings are based on the assumption that the source and target embedding spaces are structurally similar. The structures of embedding spaces largely depend on the co-occurrence statistics of each word, which the choice of context window determines. Despite this obvious connection between the context window and mapping-based cross-lingual embeddings, their relationship has been underexplored in prior work. In this work, we provide a thorough evaluation, in various languages, domains, and tasks, of bilingual embeddings trained with different context windows. The highlight of our findings is that increasing the size of both the source and target window sizes improves the performance of bilingual lexicon induction, especially the performance on frequent nouns.
%R 10.18653/v1/2020.acl-main.94
%U https://aclanthology.org/2020.acl-main.94
%U https://doi.org/10.18653/v1/2020.acl-main.94
%P 995-1005

Markdown (Informal)

[Revisiting the Context Window for Cross-lingual Word Embeddings](https://aclanthology.org/2020.acl-main.94) (Ri & Tsuruoka, ACL 2020)

• Revisiting the Context Window for Cross-lingual Word Embeddings (Ri & Tsuruoka, ACL 2020)

ACL

• Ryokan Ri and Yoshimasa Tsuruoka. 2020. Revisiting the Context Window for Cross-lingual Word Embeddings. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 995–1005, Online. Association for Computational Linguistics.