<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="bodapati-etal-2019-neural">
    <titleInfo>
        <title>Neural Word Decomposition Models for Abusive Language Detection</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Sravan</namePart>
        <namePart type="family">Bodapati</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Spandana</namePart>
        <namePart type="family">Gella</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kasturi</namePart>
        <namePart type="family">Bhattacharjee</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yaser</namePart>
        <namePart type="family">Al-Onaizan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Third Workshop on Abusive Language Online</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Sarah</namePart>
            <namePart type="given">T</namePart>
            <namePart type="family">Roberts</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joel</namePart>
            <namePart type="family">Tetreault</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Vinodkumar</namePart>
            <namePart type="family">Prabhakaran</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Zeerak</namePart>
            <namePart type="family">Waseem</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Florence, Italy</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The text we see in social media suffers from lots of undesired characterstics like hatespeech, abusive language, insults etc. The nature of this text is also very different compared to the traditional text we see in news with lots of obfuscated words, intended typos. This poses several robustness challenges to many natural language processing (NLP) techniques developed for traditional text. Many techniques proposed in the recent times such as charecter encoding models, subword models, byte pair encoding to extract subwords can aid in dealing with few of these nuances. In our work, we analyze the effectiveness of each of the above techniques, compare and contrast various word decomposition techniques when used in combination with others. We experiment with recent advances of finetuning pretrained language models, and demonstrate their robustness to domain shift. We also show our approaches achieve state of the art performance on Wikipedia attack, toxicity datasets, and Twitter hatespeech dataset.</abstract>
    <identifier type="citekey">bodapati-etal-2019-neural</identifier>
    <identifier type="doi">10.18653/v1/W19-3515</identifier>
    <location>
        <url>https://aclanthology.org/W19-3515</url>
    </location>
    <part>
        <date>2019-08</date>
        <extent unit="page">
            <start>135</start>
            <end>145</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Neural Word Decomposition Models for Abusive Language Detection
%A Bodapati, Sravan
%A Gella, Spandana
%A Bhattacharjee, Kasturi
%A Al-Onaizan, Yaser
%Y Roberts, Sarah T.
%Y Tetreault, Joel
%Y Prabhakaran, Vinodkumar
%Y Waseem, Zeerak
%S Proceedings of the Third Workshop on Abusive Language Online
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F bodapati-etal-2019-neural
%X The text we see in social media suffers from lots of undesired characterstics like hatespeech, abusive language, insults etc. The nature of this text is also very different compared to the traditional text we see in news with lots of obfuscated words, intended typos. This poses several robustness challenges to many natural language processing (NLP) techniques developed for traditional text. Many techniques proposed in the recent times such as charecter encoding models, subword models, byte pair encoding to extract subwords can aid in dealing with few of these nuances. In our work, we analyze the effectiveness of each of the above techniques, compare and contrast various word decomposition techniques when used in combination with others. We experiment with recent advances of finetuning pretrained language models, and demonstrate their robustness to domain shift. We also show our approaches achieve state of the art performance on Wikipedia attack, toxicity datasets, and Twitter hatespeech dataset.
%R 10.18653/v1/W19-3515
%U https://aclanthology.org/W19-3515
%U https://doi.org/10.18653/v1/W19-3515
%P 135-145

Markdown (Informal)

[Neural Word Decomposition Models for Abusive Language Detection](https://aclanthology.org/W19-3515) (Bodapati et al., ALW 2019)

• Neural Word Decomposition Models for Abusive Language Detection (Bodapati et al., ALW 2019)

ACL

• Sravan Bodapati, Spandana Gella, Kasturi Bhattacharjee, and Yaser Al-Onaizan. 2019. Neural Word Decomposition Models for Abusive Language Detection. In Proceedings of the Third Workshop on Abusive Language Online, pages 135–145, Florence, Italy. Association for Computational Linguistics.