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    <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.</abstract>
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%0 Conference Proceedings
%T RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generators
%A Adewoyin, Rilwan
%A Dutta, Ritabrata
%A He, Yulan
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F adewoyin-etal-2022-rstgen
%X 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.
%R 10.18653/v1/2022.naacl-main.133
%U https://aclanthology.org/2022.naacl-main.133
%U https://doi.org/10.18653/v1/2022.naacl-main.133
%P 1822-1835

Markdown (Informal)

[RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generators](https://aclanthology.org/2022.naacl-main.133) (Adewoyin et al., NAACL 2022)

• RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generators (Adewoyin et al., NAACL 2022)

ACL

• Rilwan Adewoyin, Ritabrata Dutta, and Yulan He. 2022. RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generators. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1822–1835, Seattle, United States. Association for Computational Linguistics.