<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="nguyen-etal-2020-wnut">
    <titleInfo>
        <title>WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Dat</namePart>
        <namePart type="given">Quoc</namePart>
        <namePart type="family">Nguyen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Thanh</namePart>
        <namePart type="family">Vu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Afshin</namePart>
        <namePart type="family">Rahimi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mai</namePart>
        <namePart type="given">Hoang</namePart>
        <namePart type="family">Dao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Linh</namePart>
        <namePart type="given">The</namePart>
        <namePart type="family">Nguyen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Long</namePart>
        <namePart type="family">Doan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-nov</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper, we provide an overview of the WNUT-2020 shared task on the identification of informative COVID-19 English Tweets. We describe how we construct a corpus of 10K Tweets and organize the development and evaluation phases for this task. In addition, we also present a brief summary of results obtained from the final system evaluation submissions of 55 teams, finding that (i) many systems obtain very high performance, up to 0.91 F1 score, (ii) the majority of the submissions achieve substantially higher results than the baseline fastText (Joulin et al., 2017), and (iii) fine-tuning pre-trained language models on relevant language data followed by supervised training performs well in this task.</abstract>
    <identifier type="citekey">nguyen-etal-2020-wnut</identifier>
    <identifier type="doi">10.18653/v1/2020.wnut-1.41</identifier>
    <location>
        <url>https://aclanthology.org/2020.wnut-1.41</url>
    </location>
    <part>
        <date>2020-nov</date>
        <extent unit="page">
            <start>314</start>
            <end>318</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets
%A Nguyen, Dat Quoc
%A Vu, Thanh
%A Rahimi, Afshin
%A Dao, Mai Hoang
%A Nguyen, Linh The
%A Doan, Long
%S Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F nguyen-etal-2020-wnut
%X In this paper, we provide an overview of the WNUT-2020 shared task on the identification of informative COVID-19 English Tweets. We describe how we construct a corpus of 10K Tweets and organize the development and evaluation phases for this task. In addition, we also present a brief summary of results obtained from the final system evaluation submissions of 55 teams, finding that (i) many systems obtain very high performance, up to 0.91 F1 score, (ii) the majority of the submissions achieve substantially higher results than the baseline fastText (Joulin et al., 2017), and (iii) fine-tuning pre-trained language models on relevant language data followed by supervised training performs well in this task.
%R 10.18653/v1/2020.wnut-1.41
%U https://aclanthology.org/2020.wnut-1.41
%U https://doi.org/10.18653/v1/2020.wnut-1.41
%P 314-318

Markdown (Informal)

[WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets](https://aclanthology.org/2020.wnut-1.41) (Nguyen et al., WNUT 2020)

• WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets (Nguyen et al., WNUT 2020)

ACL

• Dat Quoc Nguyen, Thanh Vu, Afshin Rahimi, Mai Hoang Dao, Linh The Nguyen, and Long Doan. 2020. WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 314–318, Online. Association for Computational Linguistics.