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 algorithms).

• AKA: Domain-Specific NLG Writing, Specialized Automated Content Generation, Vertical-Specific Text Generation Task, Industry-Specific Automated Writing.
• Context:
  • Task Input: domain knowledge base, writing parameters, domain-specific requirements
    • Optional Input: domain-specific templates, reference documents, style guides, terminology databases
  • Task Output: domain-specific written content, metadata, machine written text tailored to a specific 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 Standards through specialized formatting rules.
  • It can typically maintain Domain Writing Coherence through specialized linguistic patterns.
  • It can typically incorporate Technical Accuracy with domain-specific fact checking.
  • ...
  • It can often utilize Domain-Specific Writing Templates for structure consistency.
  • It can often leverage Domain Writing Corpus for language patterns.
  • It can often implement Domain Writing Rules for content validation.
  • It can often support Domain Writing Workflows 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.
  • ...
• Examples:
  • Automated Domain-Specific Medical Writing Tasks, 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 Tasks, 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 Tasks, 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 Tasks, 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 Tasks, 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 Tasks, 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.
  • ...
• Counter-Examples:
  • 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.
• See: Automated Writing Task, Domain-Specific Task, Specialized Text Generation Task, Automated Content Generation Task, Industry-Specific Writing Task, Automated Text Generation.

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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). "Dolomites: Domain-Specific Long-Form Methodical Tasks". In: arXiv preprint arXiv:2405.05938.
  • QUOTE: Experts in various fields routinely perform methodical writing tasks to plan, organize, and report their work.

    From a clinician writing a differential diagnosis for a patient, to a teacher writing a lesson plan for students, these tasks are pervasive, requiring to methodically generate structured long-form output for a given input.

    We develop a typology of methodical tasks structured in the form of a task objective, procedure, input, and output, and introduce DoLoMiTes, a novel benchmark with specifications for 519 such tasks elicited from hundreds of experts from across 25 fields.

    Our benchmark further contains specific instantiations of methodical tasks with concrete input and output examples (1,857 in total) which we obtain by collecting expert revisions of up to 10 model-generated examples of each task.

    We use these examples to evaluate contemporary language models highlighting that automating methodical tasks is a challenging long-form generation problem, as it requires performing complex inferences, while drawing upon the given context as well as domain knowledge.

2024b

• (Chamoun et al., 2024) ⇒ Eric Chamoun, Michael Schlichtkrull, and Andreas Vlachos (2024). "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 researchers who often rely on feedback from experienced peers.

    Recent work has primarily focused on improving surface form and style rather than manuscript content.

    In this paper, we propose a novel task: automated focused feedback generation for scientific writing assistance.

    We present SWIF²T: a Scientific Writing Focused Feedback Tool.

    It is designed to generate specific, actionable and coherent comments, which identify weaknesses in a scientific paper and/or propose revisions to it.

    Our approach consists of four components—planner, investigator, reviewer and controller—leveraging multiple Large Language Models (LLMs) to implement them.

    We compile a dataset of 300 peer reviews citing weaknesses in scientific papers and conduct human evaluation.

    The results demonstrate the superiority in specificity, reading comprehension, and overall helpfulness of SWIF²T's feedback compared to other approaches.

    In our analysis, we also identified cases where automatically generated reviews were judged better than human ones, suggesting 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). "Human-AI Collaborative Taxonomy Construction: A Case Study in Profession-Specific Writing Assistants". In: arXiv preprint arXiv:2406.18675.
  • QUOTE: Large Language Models (LLMs) have assisted humans in several writing tasks, including text revision and story generation.

    However, their effectiveness in supporting domain-specific writing, particularly in business contexts, is relatively less explored.

    Our formative study with industry professionals revealed the limitations in current LLMs' understanding of the nuances in such domain-specific writing.

    To address this gap, we propose an approach of human-AI collaborative taxonomy development to perform as a guideline for domain-specific writing assistants.

    This method integrates iterative feedback from domain experts and multiple interactions between these experts and LLMs to refine the taxonomy.

    Through larger-scale experiments, we aim to validate this methodology and thus improve LLM-powered writing assistance, tailoring it to meet the unique requirements of different stakeholder needs.

2021a

• (Kaur et al., 2021) ⇒ Harleen Kaur, Steve Whittaker, and John M. Carroll (2021). "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 approaches cannot currently break down a task into subtasks because of the lack of natural language understanding, or because they are missing process-level structural information about complex domains.

2021b

• (Feng and Chukharev-Hudilainen, 2021) ⇒ Hui Feng and Evgeny Chukharev-Hudilainen (2021). "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).

    In particular, their tool was designed to evaluate the writing of research abstracts by graduate students of engineering in a Taiwanese university.

    The custom-developed tool, a module for the CyWrite system developed at Iowa State University, provides feedback on lexical bundles and verb forms (tense, aspect, and voice) in line with the findings of previous research into functional moves and steps in this specific sub-genre.

2021c

• (Dong et al., 2021) ⇒ Feng Dong, Yanyan Zou, and Xiaojie Wang (2021). "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.

    Experimental results show that this approach is better than the previous neural network methods.

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