View source for Automated Domain-Specific Writing Task

An [[Automated Domain-Specific Writing Task]] is a [[domain-specific writing task]] that is an [[automated writing task]] (supported by an [[automated domain-specific writing system]] that implements [[domain-specific writing algorithm]]s).
* <B>AKA:</B> [[Automated Domain-Specific Writing Task|Domain-Specific NLG Writing]], [[Automated Domain-Specific Writing Task|Specialized Automated Content Generation]], [[Automated Domain-Specific Writing Task|Vertical-Specific Text Generation Task]], [[Automated Domain-Specific Writing Task|Industry-Specific Automated Writing]].
* <B>Context:</B>
** [[Task Input]]: [[domain knowledge base]], [[writing parameter]]s, [[domain-specific requirement]]s
*** [[Optional Input]]: [[domain-specific template]]s, [[reference document]]s, [[style guides]], [[terminology database]]s
** [[Task Output]]: [[domain-specific written content]], [[metadata]], [[machine written text]] tailored to a specific [[Research Domain|domain]]
** [[Task Performance Measure]]: [[domain relevance]], [[technical accuracy]], [[domain-specific writing performance measure]], [[terminology correctness]], [[stylistic adherence]]
** It can typically process [[Domain Knowledge]] with [[specialized terminology]].
** It can typically produce [[Domain-Specific Written Content]] with [[domain conventions]].
** It can typically adhere to [[Domain Writing Standard]]s through [[specialized formatting rule]]s.
** It can typically maintain [[Domain Writing Coherence]] through [[specialized linguistic pattern]]s.
** It can typically incorporate [[Technical Accuracy]] with [[domain-specific fact checking]].
** ...
** It can often utilize [[Domain-Specific Writing Template]]s for [[structure consistency]].
** It can often leverage [[Domain Writing Corpus]] for [[language patterns]].
** It can often implement [[Domain Writing Rule]]s for [[content validation]].
** It can often support [[Domain Writing Workflow]]s through [[integration capabilities]].
** It can often adapt [[Domain Writing Style]] to match [[domain conventions]].
** ...
** It can range from being a [[Narrow Domain Writing Task]] to being a [[Broad Domain Writing Task]], based on [[domain scope]].
** It can range from being a [[Terminology-Focused Writing Task]] to being a [[Context-Focused Writing Task]], based on [[domain knowledge depth]].
** It can range from being a [[Single-Purpose Domain Writing Task]] to being a [[Multi-Purpose Domain Writing Task]], based on [[task versatility]].
** It can range from being a [[Short-form Domain Writing Task]] to being a [[Long-form Domain Writing Task]], based on [[text length]].
** It can range from being a [[Constrained Domain-Specific Writing Task]] to being an [[Unconstrained Domain-Specific Writing Task]], depending on [[content freedom]].
** It can range from being a [[Template-Based Writing Generation Task]] to being an [[AI-Powered Writing System Task]], depending on [[generation sophistication]].
** ...
** It can be solved by a [[Domain-Specific Writing System]] that implements a [[domain-specific writing generation algorithm]].
** It can be supported by a [[Domain-Specific Text Understanding Task]] for [[content validation]].
** It can integrate with [[Domain Knowledge Management Systems]] for [[domain knowledge access]].
** It can connect to [[Domain Content Databases]] for [[factual information]].
** It can support [[Domain Content Management Systems]] for [[output publishing]].
** It can work with [[Domain Expert Writing Systems]] for [[validation processes]].
** ...
* <B>Examples:</B>
** [[Automated Domain-Specific Medical Writing Task]]s, such as:
*** [[Automated Domain-Specific Medical Report Writing Task]] for [[patient documentation]].
*** [[Automated Domain-Specific Drug Description Writing Task]] for [[pharmaceutical information]].
*** [[Automated Domain-Specific Patient Discharge Summary Writing Task]] for [[care continuity]].
*** [[Automated Domain-Specific Clinical Protocol Writing Task]] for [[treatment guidelines]].
** [[Automated Domain-Specific Legal Writing Task]]s, such as:
*** [[Automated Domain-Specific Legal Document Writing Task]] for [[legal proceedings]].
*** [[Automated Domain-Specific Contract Clause Writing Task]] for [[legal agreements]].
*** [[Automated Domain-Specific Legal Case Summary Writing Task]] for [[case research]].
*** [[Automated Domain-Specific Legal Brief Writing Task]] for [[court submissions]].
** [[Automated Domain-Specific Financial Writing Task]]s, such as:
*** [[Automated Domain-Specific Financial Report Writing Task]] for [[investor communications]].
*** [[Automated Domain-Specific Stock Market Analysis Writing Task]] for [[investment decisions]].
*** [[Automated Domain-Specific Investment Advice Writing Task]] for [[client recommendations]].
*** [[Automated Domain-Specific Financial Compliance Document Writing Task]] for [[regulatory requirements]].
** [[Automated Domain-Specific Technical Writing Task]]s, such as:
*** [[Automated Domain-Specific API Documentation Writing Task]] for [[developer reference]].
*** [[Automated Domain-Specific Bug Report Writing Task]] for [[development priorities]].
*** [[Automated Domain-Specific Technical Specification Writing Task]] for [[product development]].
*** [[Automated Domain-Specific User Manual Writing Task]] for [[end-user instruction]].
** [[Automated Domain-Specific Wikitext Writing Task]]s, such as:
*** [[Automated Domain-Specific Wiki Article Writing Task]] for [[knowledge base expansion]].
*** [[Automated Domain-Specific Wiki Template Generation Task]] for [[content standardization]].
*** [[Automated Domain-Specific Wiki Category Population Task]] for [[information organization]].
*** [[Automated Domain-Specific Structured Wiki Content Writing Task]] for [[semantic knowledge representation]].
*** [[Automated Domain-Specific Wiki Reference Generation Task]] for [[citation management]].
** [[Automated Domain-Specific Educational Writing Task]]s, such as:
*** [[Automated Domain-Specific Lesson Plan Writing Task]] for [[curriculum development]].
*** [[Automated Domain-Specific Student Feedback Writing Task]] for [[learning improvement]].
*** [[Automated Domain-Specific Educational Quiz Writing Task]] for [[knowledge assessment]].
*** [[Automated Domain-Specific Educational Content Adaptation Task]] for [[different learning levels]].
** ...
* <B>Counter-Examples:</B>
** [[Domain-Specific Text Understanding Task]], which focuses on [[understanding]] rather than [[generating]] content.
** [[Automated Open-Domain Writing Task]], which lacks [[domain-specific knowledge application]].
** [[Automated General-Purpose Text Summarization Task]], which is not [[tailored to specific domains]].
** [[Automated Multi-Domain Text Translation Task]], which focuses on [[language conversion]] rather than [[original content creation]].
** [[Automated Domain-Agnostic Writing Task]], which handles [[general content]] rather than [[specialized content]].
** [[Human-Performed Domain-Specific Writing Task]], which lacks [[automation components]].
* <B>See:</B> [[Automated Writing Task]], [[Domain-Specific Task]], [[Specialized Text Generation Task]], [[Automated Content Generation Task]], [[Industry-Specific Writing Task]], [[Automated Text Generation]], [[Automated Content Creation]].
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== References ==  

=== 2024a ===
* (Malaviya et al., 2024) ⇒ [[Chaitanya Malaviya]], [[Priyanka Agrawal]], [[Kuzman Ganchev]], [[Pranesh Srinivasan]], [[Fantine Huot]], [[Jonathan Berant]], [[Mark Yatskar]], [[Dipanjan Das]], [[Mirella Lapata]], and [[Chris Alberti]] (2024). [https://arxiv.org/abs/2405.05938 "Dolomites: Domain-Specific Long-Form Methodical Tasks"]. In: [[arXiv preprint]] [[arXiv]]:2405.05938.
** QUOTE: [[Expert]]s in various [[field]]s routinely perform [[methodical writing task]]s to [[plan]], [[organize]], and [[report]] their [[work]].<P> From a [[clinician]] writing a [[differential diagnosis]] for a [[patient]], to a [[teacher]] writing a [[lesson plan]] for [[student]]s, these tasks are pervasive, requiring to methodically generate structured [[long-form]] [[output]] for a given [[input]].<P> We develop a [[typology]] of [[methodical task]]s structured in the form of a [[task objective]], [[procedure]], [[input]], and [[output]], and introduce [[DoLoMiTes]], a novel [[benchmark]] with specifications for 519 [[Domain-Specific Long-Form Methodical Task|such task]]s elicited from hundreds of [[expert]]s from across 25 [[field]]s.<P> Our [[benchmark]] further contains specific instantiations of [[methodical task]]s with concrete [[input]] and [[output]] [[example]]s (1,857 in total) which we obtain by collecting [[expert revision]]s of up to 10 [[model-generated example]]s of each [[task]].<P> We use these [[example]]s to evaluate contemporary [[language model]]s highlighting that [[automating]] [[methodical task]]s is a challenging [[long-form generation problem]], as it requires performing [[complex inference]]s, while drawing upon the given [[context]] as well as [[domain knowledge]].

=== 2024b ===
* (Chamoun et al., 2024) ⇒ [[Eric Chamoun]], [[Michael Schlichtkrull]], and [[Andreas Vlachos]] (2024). [https://aclanthology.org/2024.findings-acl.580/ "Automated Focused Feedback Generation for Scientific Writing Assistance"]. In: [[Findings of the Association for Computational Linguistics]]: [[ACL 2024]].
** QUOTE: [[Scientific writing]] is a challenging [[task]], particularly for [[novice researcher]]s who often rely on [[feedback]] from [[experienced peer]]s.<P> Recent [[work]] has primarily focused on improving [[surface form]] and [[style]] rather than [[manuscript content]].<P> In this [[paper]], we propose a novel [[task]]: [[automated focused feedback generation]] for [[scientific writing assistance]].<P> We present [[SWIF²T]]: a [[Scientific Writing Focused Feedback Tool]].<P> It is designed to generate [[specific]], [[actionable]] and [[coherent comment]]s, which identify [[weakness]]es in a [[scientific paper]] and/or propose [[revision]]s to it.<P> Our [[approach]] consists of [[four]] [[component]]s—[[planner]], [[investigator]], [[reviewer]] and [[controller]]—leveraging multiple [[Large Language Model]]s ([[LLM]]s) to implement them.<P> We compile a [[dataset]] of [[300]] [[peer review]]s citing [[weakness]]es in [[scientific paper]]s and conduct [[human evaluation]].<P> The results demonstrate the superiority in [[specificity]], [[reading comprehension]], and overall [[helpfulness]] of [[SWIF²T]]'s [[feedback]] compared to other [[approach]]es.<P> In our [[analysis]], we also identified [[case]]s where [[automatically generated review]]s were judged [[better]] than [[human]] ones, suggesting [[opportunity|opportunities]] for integration of [[AI-generated feedback]] in [[scientific writing]].

=== 2024c ===
* (Lee et al., 2024) ⇒ [[Minhwa Lee]], [[Zae Myung Kim]], [[Vivek Khetan]], and [[Dongyeop Kang]] (2024). [https://arxiv.org/abs/2406.18675 "Human-AI Collaborative Taxonomy Construction: A Case Study in Profession-Specific Writing Assistants"]. In: [[arXiv preprint]] [[arXiv]]:2406.18675.
** QUOTE: [[Large Language Model]]s ([[LLM]]s) have assisted [[human]]s in several [[writing task]]s, including [[text revision]] and [[story generation]].<P> However, their [[effectiveness]] in supporting [[domain-specific writing]], particularly in [[business context]]s, is relatively less explored.<P> Our [[formative study]] with [[industry professional]]s revealed the [[limitation]]s in current [[LLM]]s' understanding of the [[nuance]]s in such [[domain-specific writing]].<P> To address this [[gap]], we propose an [[approach]] of [[human-AI collaborative taxonomy development]] to perform as a [[guideline]] for [[domain-specific writing assistant]]s.<P> This [[method]] integrates [[iterative feedback]] from [[domain expert]]s and multiple [[interaction]]s between these [[expert]]s and [[LLM]]s to refine the [[taxonomy]].<P> Through [[larger-scale experiment]]s, we aim to validate this [[methodology]] and thus improve [[LLM-powered writing assistance]], tailoring it to meet the unique [[requirement]]s of different [[stakeholder]] [[need]]s.

=== 2021a ===
* (Kaur et al., 2021) ⇒ [[Harleen Kaur]], [[Steve Whittaker]], and [[John M. Carroll]] (2021). [https://www.microsoft.com/en-us/research/uploads/prod/2018/10/action-plans.pdf "Creating Better Action Plans for Writing Tasks via Vocabulary-Based Planning"]. In: [[Proceedings of the ACM on Human-Computer Interaction]], Vol. 2, No. CSCW, Article 86.
** QUOTE: [[Fully-automated approach]]es cannot currently break down a [[task]] into [[subtask]]s because of the [[lack]] of [[natural language understanding]], or because they are missing [[process-level structural information]] about [[complex domain]]s.

=== 2021b ===
* (Feng and Chukharev-Hudilainen, 2021) ⇒ [[Hui Feng]] and [[Evgeny Chukharev-Hudilainen]] (2021). [https://dr.lib.iastate.edu/bitstreams/e34b97d1-e1ea-45f5-83b4-d5c39144936b/download "Genre-based AWE System for Engineering Graduate Writing: Development and Evaluation"]. In: [[International Journal of Artificial Intelligence in Education]], 31(1), 1-28.
** QUOTE: In "[[Genre-based AWE system for engineering graduate writing: Development and evaluation]]," [[Feng]] and [[Chukharev-Hudilainen]] document an [[evaluation]] of an [[AWE system]] and accompanying [[analysis module]] that they have developed to support [[genre-specific writing]] in the domain of [[English for Specific Purposes]] ([[ESP]]).<P> In particular, their [[tool]] was designed to evaluate the [[writing]] of [[research abstract]]s by [[graduate student]]s of [[engineering]] in a [[Taiwanese university]].<P> The [[custom-developed tool]], a [[module]] for the [[CyWrite system]] developed at [[Iowa State University]], provides [[feedback]] on [[lexical bundle]]s and [[verb form]]s ([[tense]], [[aspect]], and [[voice]]) in line with the findings of previous [[research]] into [[functional move]]s and [[step]]s in this specific [[sub-genre]].<P> 

=== 2021c ===
* (Dong et al., 2021) ⇒ [[Feng Dong]], [[Yanyan Zou]], and [[Xiaojie Wang]] (2021). [https://ijcsmc.com/docs/papers/October2021/V10I10202108.pdf "Domain Specific Automated Essay Scoring Using Cloud Based NLP API"]. In: [[International Journal of Computer Science and Mobile Computing]], 10(10), 66-74.
** QUOTE: The proposed [[model]] is used for the [[task]] of [[automated essay scoring]] and takes the [[Automated Student Assessment Prize dataset]] as [[evaluation]].<P> [[Experimental result]]s show that this [[approach]] is better than the previous [[neural network]] methods.

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__NOTOC__  
[[Category:Concept]]  
[[Category:Artificial Intelligence]]  
[[Category:Natural Language Processing]]  
[[Category:Text Processing]]
[[Category:Task Type]]
[[Category:Quality Silver]]