@inproceedings{adewoyin-etal-2022-rstgen,
    title = "{RSTG}en: Imbuing Fine-Grained Interpretable Control into Long-{F}orm{T}ext Generators",
    author = "Adewoyin, Rilwan  and
      Dutta, Ritabrata  and
      He, Yulan",
    editor = "Carpuat, Marine  and
      de Marneffe, Marie-Catherine  and
      Meza Ruiz, Ivan Vladimir",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2022.naacl-main.133/",
    doi = "10.18653/v1/2022.naacl-main.133",
    pages = "1822--1835",
    abstract = "In this paper, we study the task of improving the cohesion and coherence of long-form text generated by language models. To this end, we propose RSTGen, a framework that utilises Rhetorical Structure Theory (RST), a classical language theory, to control the discourse structure, semantics and topics of generated text. Firstly, we demonstrate our model{'}s ability to control structural discourse and semantic features of generated text in open generation evaluation. Then we experiment on the two challenging long-form text tasks of argument generation and story generation. Evaluation using automated metrics and a metric with high correlation to human evaluation, shows that our model performs competitively against existing models, while offering significantly more controls over generated text than alternative methods."
}