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        <title>ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents</title>
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    <name type="personal">
        <namePart type="given">Kellin</namePart>
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    <name type="personal">
        <namePart type="given">Jacob</namePart>
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    <name type="personal">
        <namePart type="given">Albert</namePart>
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    <name type="personal">
        <namePart type="given">Reihaneh</namePart>
        <namePart type="family">Rabbany</namePart>
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            <title>Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)</title>
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        <name type="personal">
            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
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        <name type="personal">
            <namePart type="given">Alan</namePart>
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            <namePart type="given">Tim</namePart>
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            <namePart type="given">Afshin</namePart>
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    <abstract>Given the global scale of COVID-19 and the flood of social media content related to it, how can we find informative discussions? We present Gapformer, which effectively classifies content as informative or not. It reformulates the problem as graph classification, drawing on not only the tweet but connected webpages and entities. We leverage a pre-trained language model as well as the connections between nodes to learn a pooled representation for each document network. We show it outperforms several competitive baselines and present ablation studies supporting the benefit of the linked information. Code is available on Github.</abstract>
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            <start>434</start>
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%0 Conference Proceedings
%T ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents
%A Pelrine, Kellin
%A Danovitch, Jacob
%A Orozco Camacho, Albert
%A Rabbany, Reihaneh
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F pelrine-etal-2020-complexdatalab
%X Given the global scale of COVID-19 and the flood of social media content related to it, how can we find informative discussions? We present Gapformer, which effectively classifies content as informative or not. It reformulates the problem as graph classification, drawing on not only the tweet but connected webpages and entities. We leverage a pre-trained language model as well as the connections between nodes to learn a pooled representation for each document network. We show it outperforms several competitive baselines and present ablation studies supporting the benefit of the linked information. Code is available on Github.
%R 10.18653/v1/2020.wnut-1.63
%U https://aclanthology.org/2020.wnut-1.63
%U https://doi.org/10.18653/v1/2020.wnut-1.63
%P 434-439

Markdown (Informal)

[ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents](https://aclanthology.org/2020.wnut-1.63) (Pelrine et al., WNUT 2020)

• ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents (Pelrine et al., WNUT 2020)

ACL

• Kellin Pelrine, Jacob Danovitch, Albert Orozco Camacho, and Reihaneh Rabbany. 2020. ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 434–439, Online. Association for Computational Linguistics.