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        <title>Understanding Politics via Contextualized Discourse Processing</title>
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        <namePart type="given">Rajkumar</namePart>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</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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    <abstract>Politicians often have underlying agendas when reacting to events. Arguments in contexts of various events reflect a fairly consistent set of agendas for a given entity. In spite of recent advances in Pretrained Language Models, those text representations are not designed to capture such nuanced patterns. In this paper, we propose a Compositional Reader model consisting of encoder and composer modules, that captures and leverages such information to generate more effective representations for entities, issues, and events. These representations are contextualized by tweets, press releases, issues, news articles, and participating entities. Our model processes several documents at once and generates composed representations for multiple entities over several issues or events. Via qualitative and quantitative empirical analysis, we show that these representations are meaningful and effective.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.102</identifier>
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        <date>2021-11</date>
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%0 Conference Proceedings
%T Understanding Politics via Contextualized Discourse Processing
%A Pujari, Rajkumar
%A Goldwasser, Dan
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F pujari-goldwasser-2021-understanding
%X Politicians often have underlying agendas when reacting to events. Arguments in contexts of various events reflect a fairly consistent set of agendas for a given entity. In spite of recent advances in Pretrained Language Models, those text representations are not designed to capture such nuanced patterns. In this paper, we propose a Compositional Reader model consisting of encoder and composer modules, that captures and leverages such information to generate more effective representations for entities, issues, and events. These representations are contextualized by tweets, press releases, issues, news articles, and participating entities. Our model processes several documents at once and generates composed representations for multiple entities over several issues or events. Via qualitative and quantitative empirical analysis, we show that these representations are meaningful and effective.
%R 10.18653/v1/2021.emnlp-main.102
%U https://aclanthology.org/2021.emnlp-main.102
%U https://doi.org/10.18653/v1/2021.emnlp-main.102
%P 1353-1367

Markdown (Informal)

[Understanding Politics via Contextualized Discourse Processing](https://aclanthology.org/2021.emnlp-main.102) (Pujari & Goldwasser, EMNLP 2021)

• Understanding Politics via Contextualized Discourse Processing (Pujari & Goldwasser, EMNLP 2021)

ACL

• Rajkumar Pujari and Dan Goldwasser. 2021. Understanding Politics via Contextualized Discourse Processing. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1353–1367, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.