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        <title>Fine-Grained Propaganda Detection with Fine-Tuned BERT</title>
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    <name type="personal">
        <namePart type="given">Shehel</namePart>
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    <name type="personal">
        <namePart type="given">Yin</namePart>
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            <title>Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda</title>
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        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Feldman</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Giovanni</namePart>
            <namePart type="family">Da San Martino</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Alberto</namePart>
            <namePart type="family">Barrón-Cedeño</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Chris</namePart>
            <namePart type="family">Brew</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Chris</namePart>
            <namePart type="family">Leberknight</namePart>
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        <name type="personal">
            <namePart type="given">Preslav</namePart>
            <namePart type="family">Nakov</namePart>
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    <abstract>This paper presents the winning solution of the Fragment Level Classification (FLC) task in the Fine Grained Propaganda Detection competition at the NLP4IF’19 workshop. The goal of the FLC task is to detect and classify textual segments that correspond to one of the 18 given propaganda techniques in a news articles dataset. The main idea of our solution is to perform word-level classification using fine-tuned BERT, a popular pre-trained language model. Besides presenting the model and its evaluation results, we also investigate the attention heads in the model, which provide insights into what the model learns, as well as aspects for potential improvements.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5011</identifier>
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%0 Conference Proceedings
%T Fine-Grained Propaganda Detection with Fine-Tuned BERT
%A Yoosuf, Shehel
%A Yang, Yin
%Y Feldman, Anna
%Y Da San Martino, Giovanni
%Y Barrón-Cedeño, Alberto
%Y Brew, Chris
%Y Leberknight, Chris
%Y Nakov, Preslav
%S Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F yoosuf-yang-2019-fine
%X This paper presents the winning solution of the Fragment Level Classification (FLC) task in the Fine Grained Propaganda Detection competition at the NLP4IF’19 workshop. The goal of the FLC task is to detect and classify textual segments that correspond to one of the 18 given propaganda techniques in a news articles dataset. The main idea of our solution is to perform word-level classification using fine-tuned BERT, a popular pre-trained language model. Besides presenting the model and its evaluation results, we also investigate the attention heads in the model, which provide insights into what the model learns, as well as aspects for potential improvements.
%R 10.18653/v1/D19-5011
%U https://aclanthology.org/D19-5011
%U https://doi.org/10.18653/v1/D19-5011
%P 87-91

Markdown (Informal)

[Fine-Grained Propaganda Detection with Fine-Tuned BERT](https://aclanthology.org/D19-5011) (Yoosuf & Yang, NLP4IF 2019)

• Fine-Grained Propaganda Detection with Fine-Tuned BERT (Yoosuf & Yang, NLP4IF 2019)

ACL

• Shehel Yoosuf and Yin Yang. 2019. Fine-Grained Propaganda Detection with Fine-Tuned BERT. In Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 87–91, Hong Kong, China. Association for Computational Linguistics.