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Cited by 81Natural Language Processing TechniquesTopic ModelingSpeech and dialogue systems

The automated writing assistance landscape in 2021

Open access
Dale, Robert, Viethen, Jette·2021·Natural Language Engineering
DOI 10.1017/s1351324921000164
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Abstract

Abstract Automated writing assistance – a category that encompasses a variety of computer-based tools that help with writing – has been around in one form or another for 60 years, although it’s always been a relatively minor part of the NLP landscape. But the category has been given a substantial boost from recent advances in deep learning. We review some history, look at where things stand today, and consider where things might be going.

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Crossref · 10.1017/s1351324921000164

Cites

9 of 9 references verified0 citation errors · 0 not checkable

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  • It is highly likely that LLMs will be integrated more deeply into suites of products that are often provided to students by HEIs in the future.

    “it is highly likely that LLMs will be integrated more deeply into suites of products that are often provided to students by HEIs in the future”

    From Academic Integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond (2023) · cites this paper as Dale & Viethen, 2021

    In the concluding “Copy Shark” passage (page unavailable), the paper predicts that GPT-3-based writing capabilities may soon appear in Microsoft Word. That supports future LLM integration into a widely used product, but the paper does not say that such integration is highly likely across product suites provided to students by higher education institutions. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • LLMs will generally always provide an answer or output, even if that is factually incorrect or misinterpreted.

    “Although one concern levied against LLMs is that they will generally always provide an answer or output, even if that is factually incorrect or misinterpreted (Dale, 2021; Hutson, 2022)”

    From Academic Integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond (2023) · cites this paper as Dale, 2021

    Section 3.3, “From editor to author” (page unavailable), says GPT-3 can generate convincing text that is sometimes nonsense, supporting the concern that an output can be unreliable. It does not say LLMs generally always provide an answer, or specifically discuss factual errors or misinterpretation. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • LLMs have the potential to produce text that lacks semantic coherence.

    “there is the potential for LLMs to produce text that lacks semantic coherence (Dale, 2021)”

    From Academic Integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond (2023) · cites this paper as Dale, 2021

  • AI writing tools offered various language support functions, such as sentence or text generation, which could improve students' writing performance.

    “Similarly, Dale and Viethen (2021) reported that AI writing tools offered various language support functions, such as sentence or text generation, which could improve students' writing performance.”

    From The Role of AI-Assisted Learning in Academic Writing: A Mixed-Methods Study on Chinese as a Second Language Students (2025) · cites this paper as Dale & Viethen, 2021

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Cites

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Research works printed but not linked: 3

  1. Heidorn, G. (2000). Intelligent writing assistance. In Dale, R., Moisl H. and Somers H. (eds), A Handbook of Natural Language Processing: Techniques and Applications for the Processing of Language as Text, pp. 181-207. New York: Marcel Dekker.
  2. Macdonald, N.H. (1983). The UNIX writers workbench software: rationale and design. Bell System Technical Journal 62, 1891-1908.
  3. Naber, D. (2003). A Rule-Based Style and Grammar Checker, Diploma Thesis, University of Bielefeld.

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Authors
Dale, R., & Viethen, J.
Affiliations
Macquarie University (AU)
Corresponding author
Dale, Robert
ORCID
Dale, Robert
Venue
Natural Language Engineering
Publisher
Cambridge University Press (CUP)
Published
2021-07
Volume
27
Issue
4
Pages
511-518
ISSN
  • 1351-3249 (ISSN-L)
  • 1469-8110
DOI
10.1017/s1351324921000164
OpenAlex
W3180439001
Cited by
81
References
9
Citations by year
2026: 8 · 2025: 27 · 2024: 21 · 2023: 13 · 2022: 9 · 2021: 3
FWCI
4.07
Type
journal article
Language
en
Open access
Licence
Locations
Natural Language Engineering (publishedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
Keywords
computer-assisted writing, writing support tools, natural language processing applications

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