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    <titleInfo>
        <title>Written Justifications are Key to Aggregate Crowdsourced Forecasts</title>
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        <namePart type="given">Saketh</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2021</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>This paper demonstrates that aggregating crowdsourced forecasts benefits from modeling the written justifications provided by forecasters. Our experiments show that the majority and weighted vote baselines are competitive, and that the written justifications are beneficial to call a question throughout its life except in the last quarter. We also conduct an error analysis shedding light into the characteristics that make a justification unreliable.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.355</identifier>
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%0 Conference Proceedings
%T Written Justifications are Key to Aggregate Crowdsourced Forecasts
%A Kotamraju, Saketh
%A Blanco, Eduardo
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F kotamraju-blanco-2021-written-justifications
%X This paper demonstrates that aggregating crowdsourced forecasts benefits from modeling the written justifications provided by forecasters. Our experiments show that the majority and weighted vote baselines are competitive, and that the written justifications are beneficial to call a question throughout its life except in the last quarter. We also conduct an error analysis shedding light into the characteristics that make a justification unreliable.
%R 10.18653/v1/2021.findings-emnlp.355
%U https://aclanthology.org/2021.findings-emnlp.355
%U https://doi.org/10.18653/v1/2021.findings-emnlp.355
%P 4206-4216

Markdown (Informal)

[Written Justifications are Key to Aggregate Crowdsourced Forecasts](https://aclanthology.org/2021.findings-emnlp.355) (Kotamraju & Blanco, Findings 2021)

• Written Justifications are Key to Aggregate Crowdsourced Forecasts (Kotamraju & Blanco, Findings 2021)

ACL

• Saketh Kotamraju and Eduardo Blanco. 2021. Written Justifications are Key to Aggregate Crowdsourced Forecasts. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4206–4216, Punta Cana, Dominican Republic. Association for Computational Linguistics.