<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hakimi-parizi-cook-2021-evaluating">
    <titleInfo>
        <title>Evaluating a Joint Training Approach for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora on Lower-resource Languages</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ali</namePart>
        <namePart type="family">Hakimi Parizi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Paul</namePart>
        <namePart type="family">Cook</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Lun-Wei</namePart>
            <namePart type="family">Ku</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Vivi</namePart>
            <namePart type="family">Nastase</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ivan</namePart>
            <namePart type="family">Vulić</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>Cross-lingual word embeddings provide a way for information to be transferred between languages. In this paper we evaluate an extension of a joint training approach to learning cross-lingual embeddings that incorporates sub-word information during training. This method could be particularly well-suited to lower-resource and morphologically-rich languages because it can be trained on modest size monolingual corpora, and is able to represent out-of-vocabulary words (OOVs). We consider bilingual lexicon induction, including an evaluation focused on OOVs. We find that this method achieves improvements over previous approaches, particularly for OOVs.</abstract>
    <identifier type="citekey">hakimi-parizi-cook-2021-evaluating</identifier>
    <identifier type="doi">10.18653/v1/2021.starsem-1.29</identifier>
    <location>
        <url>https://aclanthology.org/2021.starsem-1.29/</url>
    </location>
    <part>
        <date>2021-08</date>
        <extent unit="page">
            <start>302</start>
            <end>307</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Evaluating a Joint Training Approach for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora on Lower-resource Languages
%A Hakimi Parizi, Ali
%A Cook, Paul
%Y Ku, Lun-Wei
%Y Nastase, Vivi
%Y Vulić, Ivan
%S Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F hakimi-parizi-cook-2021-evaluating
%X Cross-lingual word embeddings provide a way for information to be transferred between languages. In this paper we evaluate an extension of a joint training approach to learning cross-lingual embeddings that incorporates sub-word information during training. This method could be particularly well-suited to lower-resource and morphologically-rich languages because it can be trained on modest size monolingual corpora, and is able to represent out-of-vocabulary words (OOVs). We consider bilingual lexicon induction, including an evaluation focused on OOVs. We find that this method achieves improvements over previous approaches, particularly for OOVs.
%R 10.18653/v1/2021.starsem-1.29
%U https://aclanthology.org/2021.starsem-1.29/
%U https://doi.org/10.18653/v1/2021.starsem-1.29
%P 302-307

Markdown (Informal)

[Evaluating a Joint Training Approach for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora on Lower-resource Languages](https://aclanthology.org/2021.starsem-1.29/) (Hakimi Parizi & Cook, *SEM 2021)

• Evaluating a Joint Training Approach for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora on Lower-resource Languages (Hakimi Parizi & Cook, *SEM 2021)

ACL

• Ali Hakimi Parizi and Paul Cook. 2021. Evaluating a Joint Training Approach for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora on Lower-resource Languages. In Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics, pages 302–307, Online. Association for Computational Linguistics.