<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="xia-etal-2020-bert">
    <titleInfo>
        <title>Which *BERT? A Survey Organizing Contextualized Encoders</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Patrick</namePart>
        <namePart type="family">Xia</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Shijie</namePart>
        <namePart type="family">Wu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Benjamin</namePart>
        <namePart type="family">Van Durme</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>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <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>Pretrained contextualized text encoders are now a staple of the NLP community. We present a survey on language representation learning with the aim of consolidating a series of shared lessons learned across a variety of recent efforts. While significant advancements continue at a rapid pace, we find that enough has now been discovered, in different directions, that we can begin to organize advances according to common themes. Through this organization, we highlight important considerations when interpreting recent contributions and choosing which model to use.</abstract>
    <identifier type="citekey">xia-etal-2020-bert</identifier>
    <identifier type="doi">10.18653/v1/2020.emnlp-main.608</identifier>
    <location>
        <url>https://aclanthology.org/2020.emnlp-main.608</url>
    </location>
    <part>
        <date>2020-11</date>
        <extent unit="page">
            <start>7516</start>
            <end>7533</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Which *BERT? A Survey Organizing Contextualized Encoders
%A Xia, Patrick
%A Wu, Shijie
%A Van Durme, Benjamin
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F xia-etal-2020-bert
%X Pretrained contextualized text encoders are now a staple of the NLP community. We present a survey on language representation learning with the aim of consolidating a series of shared lessons learned across a variety of recent efforts. While significant advancements continue at a rapid pace, we find that enough has now been discovered, in different directions, that we can begin to organize advances according to common themes. Through this organization, we highlight important considerations when interpreting recent contributions and choosing which model to use.
%R 10.18653/v1/2020.emnlp-main.608
%U https://aclanthology.org/2020.emnlp-main.608
%U https://doi.org/10.18653/v1/2020.emnlp-main.608
%P 7516-7533

Markdown (Informal)

[Which *BERT? A Survey Organizing Contextualized Encoders](https://aclanthology.org/2020.emnlp-main.608) (Xia et al., EMNLP 2020)

• Which *BERT? A Survey Organizing Contextualized Encoders (Xia et al., EMNLP 2020)

ACL

• Patrick Xia, Shijie Wu, and Benjamin Van Durme. 2020. Which *BERT? A Survey Organizing Contextualized Encoders. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7516–7533, Online. Association for Computational Linguistics.