RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generators
Rilwan Adewoyin, Ritabrata Dutta, Yulan He
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.
BibTeX
@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://aclanthology.org/2022.naacl-main.133/",
doi = "10.18653/v1/2022.naacl-main.133",
pages = "1822--1835"
}