<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wang-etal-2020-extending">
    <titleInfo>
        <title>Extending Multilingual BERT to Low-Resource Languages</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Zihan</namePart>
        <namePart type="family">Wang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Karthikeyan</namePart>
        <namePart type="family">K</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Stephen</namePart>
        <namePart type="family">Mayhew</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Dan</namePart>
        <namePart type="family">Roth</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2020</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Trevor</namePart>
            <namePart type="family">Cohn</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yang</namePart>
            <namePart type="family">Liu</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>Multilingual BERT (M-BERT) has been a huge success in both supervised and zero-shot cross-lingual transfer learning. However, this success is focused only on the top 104 languages in Wikipedia it was trained on. In this paper, we propose a simple but effective approach to extend M-BERT E-MBERT so it can benefit any new language, and show that our approach aids languages that are already in M-BERT as well. We perform an extensive set of experiments with Named Entity Recognition (NER) on 27 languages, only 16 of which are in M-BERT, and show an average increase of about 6% F1 on M-BERT languages and 23% F1 increase on new languages. We release models and code at http://cogcomp.org/page/publication_view/912.</abstract>
    <identifier type="citekey">wang-etal-2020-extending</identifier>
    <identifier type="doi">10.18653/v1/2020.findings-emnlp.240</identifier>
    <location>
        <url>https://aclanthology.org/2020.findings-emnlp.240/</url>
    </location>
    <part>
        <date>2020-11</date>
        <extent unit="page">
            <start>2649</start>
            <end>2656</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Extending Multilingual BERT to Low-Resource Languages
%A Wang, Zihan
%A K, Karthikeyan
%A Mayhew, Stephen
%A Roth, Dan
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F wang-etal-2020-extending
%X Multilingual BERT (M-BERT) has been a huge success in both supervised and zero-shot cross-lingual transfer learning. However, this success is focused only on the top 104 languages in Wikipedia it was trained on. In this paper, we propose a simple but effective approach to extend M-BERT E-MBERT so it can benefit any new language, and show that our approach aids languages that are already in M-BERT as well. We perform an extensive set of experiments with Named Entity Recognition (NER) on 27 languages, only 16 of which are in M-BERT, and show an average increase of about 6% F1 on M-BERT languages and 23% F1 increase on new languages. We release models and code at http://cogcomp.org/page/publication_view/912.
%R 10.18653/v1/2020.findings-emnlp.240
%U https://aclanthology.org/2020.findings-emnlp.240/
%U https://doi.org/10.18653/v1/2020.findings-emnlp.240
%P 2649-2656

Markdown (Informal)

[Extending Multilingual BERT to Low-Resource Languages](https://aclanthology.org/2020.findings-emnlp.240/) (Wang et al., Findings 2020)

• Extending Multilingual BERT to Low-Resource Languages (Wang et al., Findings 2020)

ACL

• Zihan Wang, Karthikeyan K, Stephen Mayhew, and Dan Roth. 2020. Extending Multilingual BERT to Low-Resource Languages. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2649–2656, Online. Association for Computational Linguistics.