An NLG System for Constituent Correspondence: Personality, Affect, and Alignment
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.
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