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

WS 2019 William KolkeyJian DongGreg Bybee

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... (read more)

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