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        <title>An NLG System for Constituent Correspondence: Personality, Affect, and Alignment</title>
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        <namePart type="given">William</namePart>
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            <title>Proceedings of the 12th International Conference on Natural Language Generation</title>
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            <namePart type="family">van Deemter</namePart>
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        <name type="personal">
            <namePart type="given">Chenghua</namePart>
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    <abstract>Roughly 30% of congressional staffers in the United States report spending a “great deal” of time writing responses to constituent letters. Letters often solicit an update on the status of legislation and a description of a congressman’s vote record or vote intention — structurable data that can be leveraged by a natural language generation (NLG) system to create a coherent letter response. This paper describes how PoliScribe, a pipeline-architectured NLG platform, constructs personalized responses to constituents inquiring about legislation. Emphasis will be placed on adapting NLG methodologies to the political domain, which entails special attention to affect, discursive variety, and rhetorical strategies that align a speaker with their interlocutor, even in cases of policy disagreement.</abstract>
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    <identifier type="doi">10.18653/v1/W19-8631</identifier>
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        <date>2019-oct–nov</date>
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%0 Conference Proceedings
%T An NLG System for Constituent Correspondence: Personality, Affect, and Alignment
%A Kolkey, William
%A Dong, Jian
%A Bybee, Greg
%Y van Deemter, Kees
%Y Lin, Chenghua
%Y Takamura, Hiroya
%S Proceedings of the 12th International Conference on Natural Language Generation
%D 2019
%8 oct–nov
%I Association for Computational Linguistics
%C Tokyo, Japan
%F kolkey-etal-2019-nlg
%X Roughly 30% of congressional staffers in the United States report spending a “great deal” of time writing responses to constituent letters. Letters often solicit an update on the status of legislation and a description of a congressman’s vote record or vote intention — structurable data that can be leveraged by a natural language generation (NLG) system to create a coherent letter response. This paper describes how PoliScribe, a pipeline-architectured NLG platform, constructs personalized responses to constituents inquiring about legislation. Emphasis will be placed on adapting NLG methodologies to the political domain, which entails special attention to affect, discursive variety, and rhetorical strategies that align a speaker with their interlocutor, even in cases of policy disagreement.
%R 10.18653/v1/W19-8631
%U https://aclanthology.org/W19-8631
%U https://doi.org/10.18653/v1/W19-8631
%P 240-243

Markdown (Informal)

[An NLG System for Constituent Correspondence: Personality, Affect, and Alignment](https://aclanthology.org/W19-8631) (Kolkey et al., INLG 2019)

• An NLG System for Constituent Correspondence: Personality, Affect, and Alignment (Kolkey et al., INLG 2019)

ACL

• William Kolkey, Jian Dong, and Greg Bybee. 2019. An NLG System for Constituent Correspondence: Personality, Affect, and Alignment. In Proceedings of the 12th International Conference on Natural Language Generation, pages 240–243, Tokyo, Japan. Association for Computational Linguistics.