Connectionists: 1st call for papers: WR•AI•CogS•1 (Writing Aids at the Crossroads of AI, Cognitive Science, and NLP)

[Michael Zock]

*1st call for papers*

*
*WR•AI•CogS•_1 **

Writing Aids at the Crossroads of AI, Cognitive Science, and NLP

https://sites.google.com/view/wraicogs1 
<https://sites.google.com/view/wraicogs1>

*
Co-located*with COLING 2025 (Abu Dhabi, https://coling2025.org/ 
<https://coling2025.org/%29>)<https://coling2025.org/%29>

*Paper submission deadline*: November 25, 2024

*Paper submission*: https://softconf.com/coling2025/AAC-AI25/

*

*

Þ*Keynote speaker*:
Cerstin Mahlow, Professor of Digital Linguistics and Writing Research, 
ZHAW School of Applied Linguistics, Winterthur, Switzerland

*
MOTIVATION*

This workshop is dedicated to developing writing aids grounded in human 
cognition (limitations of attention and memory, typically observed 
habits, knowledge states, and information needs). In other words, we 
focus on the cognitive and engineering aspects of interactive writing. 
Our goal is not only to help people acquire and improve their writing 
skills but also to enhance their productivity. By leveraging computer 
technology, we aim to enable them to produce better texts in less time.


Writing is one of the four cornerstones of communication. By leaving a 
trace, it allows us to reach many people, to transcend space and time, 
and to spare ourselves the trouble of memorization. Writing is 
undeniably important, whether as a communication tool, a thinking aid, 
or a memorial support. However, what is less obvious is the process—that 
is, the precise steps required to transform a vague idea into concrete, 
well-polished prose. Producing readable, well-written text requires many 
skills, deep and broad knowledge of various sorts—topic, language, 
audience, metaknowledge (how to use the information at hand?)—a lot of 
practice, and appropriate feedback.

No one can learn all this overnight. The quantity and diversity of 
knowledge to interiorize, as well as the variety of cognitive states 
encountered, may explain why writing is so difficult and why it takes 
time to gain control over the whole process and become an expert writer. 
Unfortunately, knowledge alone is not enough. Writing is also a time- 
and energy-consuming endeavor. It is very hard work.


Since writing is difficult, and since there are now computer programs 
capable of doing it, one may wonder:

(a) whether we should leave the job entirely to the machine, or

(b) whether we could use these programs to help people write or to 
acquire the skill of writing.


Indeed, there are situations where it makes sense to rely on machines 
(e.g., routine work, business letters), but there are also many 
situations where this strategy is not recommended (e.g., writing to 
understand, writing to enrich and clarify our thoughts, writing to 
support thinking). That being said, one may find a middle ground where 
humans and machines work together, each contributing their strengths. It 
remains to be seen where machines can assist in the process (e.g., idea 
generation, idea structuring, translation into language, revision, 
editing) and where it is better to leave control to humans. Hence, the 
main question is not whether we should use LLMs to produce texts, but 
rather how, when, and at what level to use them or other techniques to 
help people produce written text.

In sum, our main goal is not to substitute machines for people or to 
have them do the job in people's place, but rather to have machines 
assist people. Specifically, we aim to help people learn to write, speed 
up the process, gain better control, and reduce stress and cognitive 
load. Our motivation is largely practical and educational.

Obviously, we are not the first ones to pursue this goal. However, while 
many workshops focused on developing educational software, creating 
intelligent writing assistants, or evaluating written text, the 
submitted papers have primarily addressed formal aspects, such as 
grammatical error detection and spotting spelling mistakes. Yet good 
writing (text composition) requires much more than just the production 
of well-formed sentences.

Our mission is to go beyond merely identifying errors or mistakes made 
at the very end of the writing process, such as those due to ignorance 
or inattention. Instead, we aim to evaluate the quality of the choices 
made at higher levels. In other words, we are interested in the full 
spectrum of writing, including technology-based writing aids that 
address all tasks involved in writing: conceptual planning (ideation, 
organization), linguistic expression, editing, and revision. Hence, we 
welcome papers that focus on the higher levels of composition—such as 
thinking, reasoning, and planning (idea generation, outline planning)—as 
well as those concerned with the lower levels (grammar, spelling, and 
punctuation).


Arguably, this is the first workshop to:

a)Consider the entire spectrum of writing rather than only the lower levels,

b)Integrate humans right from the start into the development cycle of 
writing aids, and

c)Provide support and feedback at any moment —before, during, and after 
writing— rather than only at the very end.


*TOPICS*

We welcome contributions on all topics related to writing aids, 
including but not limited to the following:

*1.The Human Perspective *(cognitive scientific point of view: 
education, psycholinguistics, neuroscience)

  * Support: How can AI tools support critical thinking and logical
    reasoning in writing?How can writing assistants tailor feedback to
    individual writers, considering their unique needs and styles? How
    can we assess the quality and impact of AI-generated feedback on
    students' writing (methods, metrics, etc.)?
  * Topical coherence: How can we help people organize their ideas into
    a coherent whole? How do we model or operationalize the concept of a
    topic, the paragraph's most central element? How do we detect
    possible topics within our data? What are typical subtopics of a
    given topic, and how do we identify them? How do we cluster
    content/ideas into topics and give the clusters appropriate names?
  * Building software: How do we include humans in the development cycle
    of writing aids? How and at what level can engineers use insights
    from psycholinguistics and neuroscience? How can they model the
    writing process while accounting for human and technological factors?
  * Metacognition: What do people typically know about writing in
    general and their own writing in particular? What are their problems
    and needs? How do people manage to coordinate the different
    processes? What should an authoring ecosystem look like
    (components)? What could be automated, and what is best left for
    interactive processing?
  * Shared task: What kind of shared task would be meaningful while
    being technically feasible?

*2. The Engineering Side*

  * LLMs: Where in the writing process could we use methods developed in
    AI (e.g., LLMs) or computational linguistics (e.g., content
    generation, content structuring, translation into language,
    revision)? What are the potential benefits, dangers, and limitations
    of LLMs as writing aids? How might making the 'knowledge' of
    black-box models explicit enhance their effectiveness, particularly
    concerning the accuracy and relevance of feedback? How can we
    address challenges related to data collection, privacy, and ethical
    considerations in developing and deploying AI writing tools?
  * Tools and resources: What kind of tools and resources (e.g., Sketch
    Engine, Rhetorical Structure Theory, knowledge graphs, and linked
    data) could be useful?
  * Quality assessment: How can we check the veracity of facts,
    relevance, cohesion, coherence, style, fluency, proper use of
    pronouns, grammar, word choice, spelling, and punctuation?
  * Enhancement and evaluation: How do we enhance text analysis during
    or after writing (e.g., quality of coherence, style) using corpus
    linguistic tools? How do we evaluate or compare existing writing
    assistants (e.g., adequacy, design features, ease of use, lessons
    learned)?

*
SUBMISSION INSTRUCTIONS*

Please submit your papers via the START/SoftConf submission portal 
(https://softconf.com/coling2025/AAC-AI25/), following the COLING 2025 
templates. Submitted versions must be anonymous and should not exceed 8 
pages for long papers and 4 pages for short papers. References do not 
count toward the page limit, and may be up to 4 pages long. 
Supplementary material and appendices are also allowed. We also invite 
papers discussing tools and applications (system demonstrations) related 
to our workshop topics.


*PARTICIPATION*

The workshop requires a physical presence. If any authors are unable to 
attend and present in person, alternative arrangements (such as remote 
presentations or video recordings) may be considered. However, we cannot 
guarantee these options, as the COLING organizers and local chairs have 
informed us that they will not provide technical support or online 
access. Generally,work presented in person will be given preference over 
work presented virtually.


*WORKSHOP ORGANIZERS*

  * Michael Zock (CNRS, LIS, Aix-Marseille University, Marseille, France)
  * Kentaro Inui (Mohamed bin Zayed University of Artificial
    Intelligence, UAE; Tohoku University, Japan; RIKEN, Japan)
  * Zheng Yuan (King's College London and the University of Cambridge, UK)

*
PROGRAM COMMITTEE*

 1. Barbu Mititelu, Verginica (Research Institute for Artificial
    Intelligence, RACAI, Bucharest, Romania)
 2. Biemann, Chris (Language Technology Group, Universität Hamburg, Germany)
 3. Bryant, Christopher (Writer Inc., USA; University of Cambridge, UK)
 4. Bunt, Harry (Tilburg University, Department of Cognitive Science and
    Artificial Intelligence)
 5. Church, Ken (Northeastern University, USA)
 6. Cristea, Dan (University of Iasi, Iasi, Romania)
 7. Coyne, Steven (Tohoku University, Sendai, Japan)
 8. Dale, Robert (Language Technology Group, Church Point, NSW, Australia)
 9. Delmonte, Rodolfo (Department of Computer Science, Università Ca’
    Foscari, Italy)
10. Evert, Stephani (Computational Corpus Linguistics at
    Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany)
11. Ferret, Olivier (CEA LIST, France)
12. Fontenelle, Thierry (European Investment Bank, Luxembourg)
13. François, Thomas (CENTAL, Université catholique de Louvain, Belgium)
14. Gadeau, Gabriella (Department of Computer Science and Technology,
    University of Cambridge, UK)
15. Galván, Diana (University of Cambridge)
16. Guerraoui, Camélia (Tohoku University, Sendai, Japan)
17. Hernandez, Nicolas (University of Nantes, France)
18. Hovy, Edward (University of Melbourne, Australia, and Carnegie
    Mellon, USA)
19. Iacobacci, Ignacio (London's Speech and Semantics Lab, UK)
20. Ishii, Yutaka (Chiba University)
21. Ito, Takumi (Langsmith/Tohoku University )
22. Lafourcade, Mathieu (Université de Montpellier, France)
23. Langlais, Felipe. (DIRO/RALI, University of Montreal, Canada)
24. Mahlow, Cerstin (ZHAW School of Applied Linguistics, Winterthur,
    Switzerland)
25. Matsubayashi, Yuichiro (Tohoku University)
26. Pease, Adam (Parallax Research, Beavercreek, OH, USA)
27. Pirrelli, Vito (Institute of Computational Linguistics, University
    of Pisa)
28. Raganato, Alessandro (DISCO, University of Milano-Bicocca, Italy)
29. Redeker, Gisela (University of Groningen, The Netherlands)
30. Reed, Chris (University of Dundee, Scotland)
31. Reiter, Ehud (University of Aberdeen, Scotland)
32. Rosso, Paolo (Universitat Politècnica de València, Spain)
33. Saggion, Horacio (Universitat Pompeu Fabra, Spain)
34. Schwab, Didier (GETALP-LIG, Grenoble, France)
35. Strapparava, Carlo (Fondazione Bruno Kessler, Trento, Italy)
36. Tesfaye, Debela (University of Dundee, Scotland)
37. Wanner, Leo (Universitat Pompeu Fabra, Spain)
38. Winniwarter, Werner (CSLEARN, Educational Technologies, Vienna, Austria)
39. Zheng, Yuan (King's College London and University of Cambridge, UK)



-- 

Michael ZOCK

Emeritus Research Director CNRS
LIS UMR 7020 (Group TALEP)
Aix Marseille Université
163 avenue de Luminy - case 901
13288 Marseille / France

Mail: michael.zock at lis-lab.fr <mailto:michael.zock at lis-lab.fr>
Tel.:    +33 (0)6 51.70.97.22
Secr.:  +33 (0)4.86.09.04.60

http://pageperso.lif.univ-mrs.fr/~michael.zock/ 
<http://pageperso.lif.univ-mrs.fr/%7Emichael.zock/>

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