@inproceedings{hegel-etal-2020-substance,
    title = "Substance over {S}tyle: {D}ocument-{L}evel {T}argeted {C}ontent {T}ransfer",
    author = "Hegel, Allison  and
      Rao, Sudha  and
      Celikyilmaz, Asli  and
      Dolan, Bill",
    editor = "Webber, Bonnie  and
      Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.emnlp-main.526/",
    doi = "10.18653/v1/2020.emnlp-main.526",
    pages = "6485--6504",
    abstract = "Existing language models excel at writing from scratch, but many real-world scenarios require rewriting an existing document to fit a set of constraints. Although sentence-level rewriting has been fairly well-studied, little work has addressed the challenge of rewriting an entire document coherently. In this work, we introduce the task of document-level targeted content transfer and address it in the recipe domain, with a recipe as the document and a dietary restriction (such as vegan or dairy-free) as the targeted constraint. We propose a novel model for this task based on the generative pre-trained language model (GPT-2) and train on a large number of roughly-aligned recipe pairs. Both automatic and human evaluations show that our model out-performs existing methods by generating coherent and diverse rewrites that obey the constraint while remaining close to the original document. Finally, we analyze our model`s rewrites to assess progress toward the goal of making language generation more attuned to constraints that are substantive rather than stylistic."
}