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    <titleInfo>
        <title>A Neighborhood Framework for Resource-Lean Content Flagging</title>
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    <name type="personal">
        <namePart type="given">Sheikh</namePart>
        <namePart type="given">Muhammad</namePart>
        <namePart type="family">Sarwar</namePart>
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    <name type="personal">
        <namePart type="given">Dimitrina</namePart>
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    <name type="personal">
        <namePart type="given">Yoan</namePart>
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    <name type="personal">
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    <name type="personal">
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    <abstract>We propose a novel framework for cross- lingual content flagging with limited target- language data, which significantly outperforms prior work in terms of predictive performance. The framework is based on a nearest-neighbor architecture. It is a modern instantiation of the vanilla k-nearest neighbor model, as we use Transformer representations in all its components. Our framework can adapt to new source- language instances, without the need to be retrained from scratch. Unlike prior work on neighborhood-based approaches, we encode the neighborhood information based on query– neighbor interactions. We propose two encoding schemes and we show their effectiveness using both qualitative and quantitative analysis. Our evaluation results on eight languages from two different datasets for abusive language detection show sizable improvements of up to 9.5 F1 points absolute (for Italian) over strong baselines. On average, we achieve 3.6 absolute F1 points of improvement for the three languages in the Jigsaw Multilingual dataset and 2.14 points for the WUL dataset.</abstract>
    <identifier type="citekey">sarwar-etal-2022-neighborhood</identifier>
    <identifier type="doi">10.1162/tacl_a_00472</identifier>
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        <date>2022</date>
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            <start>484</start>
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%0 Journal Article
%T A Neighborhood Framework for Resource-Lean Content Flagging
%A Sarwar, Sheikh Muhammad
%A Zlatkova, Dimitrina
%A Hardalov, Momchil
%A Dinkov, Yoan
%A Augenstein, Isabelle
%A Nakov, Preslav
%J Transactions of the Association for Computational Linguistics
%D 2022
%V 10
%I MIT Press
%C Cambridge, MA
%F sarwar-etal-2022-neighborhood
%X We propose a novel framework for cross- lingual content flagging with limited target- language data, which significantly outperforms prior work in terms of predictive performance. The framework is based on a nearest-neighbor architecture. It is a modern instantiation of the vanilla k-nearest neighbor model, as we use Transformer representations in all its components. Our framework can adapt to new source- language instances, without the need to be retrained from scratch. Unlike prior work on neighborhood-based approaches, we encode the neighborhood information based on query– neighbor interactions. We propose two encoding schemes and we show their effectiveness using both qualitative and quantitative analysis. Our evaluation results on eight languages from two different datasets for abusive language detection show sizable improvements of up to 9.5 F1 points absolute (for Italian) over strong baselines. On average, we achieve 3.6 absolute F1 points of improvement for the three languages in the Jigsaw Multilingual dataset and 2.14 points for the WUL dataset.
%R 10.1162/tacl_a_00472
%U https://aclanthology.org/2022.tacl-1.28
%U https://doi.org/10.1162/tacl_a_00472
%P 484-502

Markdown (Informal)

[A Neighborhood Framework for Resource-Lean Content Flagging](https://aclanthology.org/2022.tacl-1.28) (Sarwar et al., TACL 2022)

• A Neighborhood Framework for Resource-Lean Content Flagging (Sarwar et al., TACL 2022)

ACL

• Sheikh Muhammad Sarwar, Dimitrina Zlatkova, Momchil Hardalov, Yoan Dinkov, Isabelle Augenstein, and Preslav Nakov. 2022. A Neighborhood Framework for Resource-Lean Content Flagging. Transactions of the Association for Computational Linguistics, 10:484–502.