<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="pham-hong-chokshi-2020-pgsg">
    <titleInfo>
        <title>PGSG at SemEval-2020 Task 12: BERT-LSTM with Tweets’ Pretrained Model and Noisy Student Training Method</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Bao-Tran</namePart>
        <namePart type="family">Pham-Hong</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Setu</namePart>
        <namePart type="family">Chokshi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Fourteenth Workshop on Semantic Evaluation</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Aurelie</namePart>
            <namePart type="family">Herbelot</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Xiaodan</namePart>
            <namePart type="family">Zhu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alexis</namePart>
            <namePart type="family">Palmer</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nathan</namePart>
            <namePart type="family">Schneider</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jonathan</namePart>
            <namePart type="family">May</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Shutova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>International Committee for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Barcelona (online)</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The paper presents a system developed for the SemEval-2020 competition Task 12 (OffensEval-2): Multilingual Offensive Language Identification in Social Media. We achieve the second place (2nd) in sub-task B: Automatic categorization of offense types and are ranked 55th with a macro F1-score of 90.59 in sub-task A: Offensive language identification. Our solution is using a stack of BERT and LSTM layers, training with the Noisy Student method. Since the tweets data contains a large number of noisy words and slang, we update the vocabulary of the BERT large model pre-trained by the Google AI Language team. We fine-tune the model with tweet sentences provided in the challenge.</abstract>
    <identifier type="citekey">pham-hong-chokshi-2020-pgsg</identifier>
    <identifier type="doi">10.18653/v1/2020.semeval-1.280</identifier>
    <location>
        <url>https://aclanthology.org/2020.semeval-1.280</url>
    </location>
    <part>
        <date>2020-12</date>
        <extent unit="page">
            <start>2111</start>
            <end>2116</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T PGSG at SemEval-2020 Task 12: BERT-LSTM with Tweets’ Pretrained Model and Noisy Student Training Method
%A Pham-Hong, Bao-Tran
%A Chokshi, Setu
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F pham-hong-chokshi-2020-pgsg
%X The paper presents a system developed for the SemEval-2020 competition Task 12 (OffensEval-2): Multilingual Offensive Language Identification in Social Media. We achieve the second place (2nd) in sub-task B: Automatic categorization of offense types and are ranked 55th with a macro F1-score of 90.59 in sub-task A: Offensive language identification. Our solution is using a stack of BERT and LSTM layers, training with the Noisy Student method. Since the tweets data contains a large number of noisy words and slang, we update the vocabulary of the BERT large model pre-trained by the Google AI Language team. We fine-tune the model with tweet sentences provided in the challenge.
%R 10.18653/v1/2020.semeval-1.280
%U https://aclanthology.org/2020.semeval-1.280
%U https://doi.org/10.18653/v1/2020.semeval-1.280
%P 2111-2116

Markdown (Informal)

[PGSG at SemEval-2020 Task 12: BERT-LSTM with Tweets’ Pretrained Model and Noisy Student Training Method](https://aclanthology.org/2020.semeval-1.280) (Pham-Hong & Chokshi, SemEval 2020)

• PGSG at SemEval-2020 Task 12: BERT-LSTM with Tweets’ Pretrained Model and Noisy Student Training Method (Pham-Hong & Chokshi, SemEval 2020)

ACL

• Bao-Tran Pham-Hong and Setu Chokshi. 2020. PGSG at SemEval-2020 Task 12: BERT-LSTM with Tweets’ Pretrained Model and Noisy Student Training Method. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 2111–2116, Barcelona (online). International Committee for Computational Linguistics.