First call for papers:
WRAICogS1 -
Writing Aids at the Crossroads of AI, Cognitive Science, and NLP
https://sites.google.com/view/wraicogs1Co-located
with COLING 2025, Abu Dhabi,
https://coling2025.org/Paper
submission deadline: November 25, 2024
Paper submission:
https://softconf.com/coling2025/AAC-AI25/*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 an intuition or 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, i.e., 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 viewpoints, including
education, psycholinguistics, and 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 could revealing the 'knowledge'
embedded within black-box models improve their effectiveness,
particularly in terms of increasing the accuracy and relevance of the
feedback they provide? 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.
Varzandeh, Mohsen (Shiraz University of Medical Sciences, Shiraz, Iran)
38.
Wanner, Leo (Universitat Pompeu Fabra, Spain)
39. Winniwarter,
Werner (CSLEARN, Educational Technologies, Vienna, Austria)
40.
Zheng, Yuan (King's College London and University of Cambridge, UK)
FOR
MORE DETAILS
* Background knowledge:
https://sites.google.com/view/wraicogs1/home/background-and-topics