Title | RSTGen: Imbuing Fine-Grained Interpretable Control into Long-Form Text Generators |
Author | |
Publication Years | 2022
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Conference Name | Conference of the North-American-Chapter-of-the-Association-for-Computational-Linguistics (NAAACL) - Human Language Technologies
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Source Title | |
Pages | 1822-1835
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Conference Date | JUL 10-15, 2022
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Conference Place | null,Seattle,WA
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Publication Place | 209 N EIGHTH STREET, STROUDSBURG, PA 18360 USA
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Publisher | |
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. |
SUSTech Authorship | Others
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Language | English
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URL | [Source Record] |
Indexed By | |
Funding Project | Engineering and Physical Sciences Research Council[EP/T017112/1];Engineering and Physical Sciences Research Council[EP/V048597/1];
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WOS Research Area | Computer Science
; Linguistics
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WOS Subject | Computer Science, Artificial Intelligence
; Computer Science, Interdisciplinary Applications
; Linguistics
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WOS Accession No | WOS:000859869501068
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Scopus EID | 2-s2.0-85138371306
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Data Source | Scopus
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Citation statistics |
Cited Times [WOS]:0
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Document Type | Conference paper |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/402773 |
Department | Department of Computer Science and Engineering |
Affiliation | 1.Department of Computer Science,University of Warwick,United Kingdom 2.Department of Statistics,University of Warwick,United Kingdom 3.The Alan Turing Institute,United Kingdom 4.Department of Computer Science and Engineering,Southern University of Science and Technology,China |
First Author Affilication | Department of Computer Science and Engineering |
Recommended Citation GB/T 7714 |
Adewoyin,Rilwan A.,Dutta,Ritabrata,He,Yulan. RSTGen: Imbuing Fine-Grained Interpretable Control into Long-Form Text Generators[C]. 209 N EIGHTH STREET, STROUDSBURG, PA 18360 USA:ASSOC COMPUTATIONAL LINGUISTICS-ACL,2022:1822-1835.
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