@inproceedings{kolkey-etal-2019-nlg,
    title = "An {NLG} System for Constituent Correspondence: Personality, Affect, and Alignment",
    author = "Kolkey, William  and
      Dong, Jian  and
      Bybee, Greg",
    editor = "van Deemter, Kees  and
      Lin, Chenghua  and
      Takamura, Hiroya",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "–" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W19-8631/",
    doi = "10.18653/v1/W19-8631",
    pages = "240--243",
    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."
}