Fwd: [Call for Papers] The First Workshop on Analogical Abstraction in Cognition, Perception, and Language (Analogy-ANGLE)

Analogy-ANGLE is a new interdisciplinary workshop co-located with IJCAI
2024 on August 3rd or 4th, 2024 in Jeju, South Korea. Analogy-ANGLE aims to
bring together researchers with an interest in analogical abstraction from
natural language processing, cognitive psychology, computer vision, deep
learning, and neuro-symbolic AI. This workshop will enable a common ground
where the complementary perspectives from these fields can come together to
form a comprehensive picture of the current landscape of analogical
abstraction, and point to standing challenges, evaluation methodologies,
and emerging techniques of interest. Thus, we organize this workshop at
IJCAI where leading researchers from the focal areas are gathered. The
multidisciplinary nature of the workshop is emphasized by the broad set of
skills of the organization team and the program committee, and the
diversity principle guiding the list of topics and the invited keynotes.\


Workshop website: https://analogy-angle.github.io/
Speakers

Ken Forbus, Northwestern University

Tony Veale, UC Dublin
Important Dates

   -

   Submission deadline: May 3rd, 2024
   -

   Notifications sent: June 4th, 2024


The deadline time is 23:59 anywhere on Earth.
Topics

We invite contributions ranging from cognitive modeling and algorithms and
methods to new tasks and new applications of analogy. Contributions that
take an interdisciplinary perspective are particularly encouraged. Topics
include (but are not limited to) the following:

   -

   Cognitive modeling
   -

      Analogy and abstraction
      -

      Analogy and conceptual metaphor
      -

      Analogy, figurative language, sarcasm, and irony
      -

      Cognitive frameworks of analogy
      -

      Cognitive/psychological studies on analogy involving human
      participants
      -

   Algorithms and methods
   -

      Studies of the analogical abilities of large language models and
      visual diffusion models
      -

      Algorithmic approaches to analogy
      -

      Augmentation and verification of large language and vision models
      through analogy
      -

      Neuro-symbolic AI architectures for analogical abstraction
      -

      Extracting analogies from knowledge bases
      -

   Tasks and benchmarks
   -

      Matching narratives and situational descriptions through narratives
      -

      Novel tasks and benchmarks for evaluating analogies in text and vision
      -

      Analogy in longer formats, e.g., narratives and videos
      -

      Analogy and visual abstraction tasks
      -

      Analogical discovery and computational creativity
      -

   Applications
   -

      Analogies for personalization, explanation, and collaboration
      -

      Novel applications of analogical abstraction
      -

      Studies of the impact of analogy in specific applications and
      domains, including education, innovation, and law

Submission Guidelines

Submissions can fall into one of the following categories:


   1.

   Full Research Papers (up to 7 pages plus 2 pages for references) -
   Papers with original research work will be judged on their technical
   soundness and rigor, though allowances made for novel or experimental
   directions. We also welcome submissions reporting negative results and
   sharing experimental insights on the technical challenges and issues of
   analogical abstraction.
   2.

   Short Papers (up to 4 pages) - Position papers or reports of ongoing
   work on new research directions.
   3.

   Dissemination Papers - Already published papers from top AI venues such
   as IJCAI, NeurIPS, AAAI, ICML, ICLR, ACL, and EMNLP that are relevant to
   the workshop. Please upload the original submission and abstract to our
   submission site.


Full and short research papers will be peer-reviewed by at least two
reviewers from the PC. Accepted full and short papers will be included in
the proceedings of the workshop. Dissemination papers will go through a
short review from the organizers, checking for their quality and relevance
to the workshop. Dissemination papers will not be included in the workshop
proceedings.

Submissions should be anonymized and the review will be double-blind.
Preprints can be stored on arXiv.
Organizing Committee

   -

   Filip Ilievski (VU Amsterdam)
   -

   Pia Sommerauer (VU Amsterdam)
   -

   Marianna Bolognesi (University of Bologna)
   -

   Ute Schmid (University of Bamberg)
   -

   Dafna Shahaf (The Hebrew University of Jerusalem)


Contact: analogy-angle@groups.google.com
Program Committee

   -

   Lisa Beinborn (VU Amsterdam)
   -

   Tommaso Caselli (RU Groningen)
   -

   Bettina Finzel (University of Bamberg)
   -

   Yifan Jiang (University of Southern California)
   -

   Kaixin Ma (Tencent AI)
   -

   Zhivar Sourati (University of Southern California)
   -

   Anna Thaler (University of Bamberg)
   -

   Riccardo Tommasini (INSA Lyon)
   -

   Piek Vossen (VU Amsterdam)
   -

   …will be extended shortly

Background

Analogical abstraction is a fundamental cognitive skill unique to humans
(Penn et al., 2008; Hofstadter, 2001), defined as the ability to perceive
and utilize the similarities between concepts, situations or events based
on (systems of) relations rather than surface similarities (Holyoak, 2012;
Gentner et al., 2012). Analogy enables creative inferences, explanations,
and generalization of knowledge, and has been used for scientific
inventions (Dunbar and Klahr, 2012), solving problems (Gick and Holyoak,
1980), and policy-making (Houghton, 1998). As such, it has been the goal of
one of the first AI programs developed by Evans (1964). It has also been
the subject of cognitive theories and studies about humans for common
processes, such as the retrieval of memories (Wharton et al., 1994) and
problem-solving (Gick and Holyoak, 1980), mostly leveraging narratives as
their experimental medium (Gentner and Toupin, 1986; Gentner et al., 1993;
Wharton et al., 1994), given their multi-tiered nature and potential for
abstraction.

Meanwhile, analogical tasks have also been a relatively popular topic in
natural language processing (NLP) and artificial intelligence (AI),
typically framed as intelligence tests for models compared against humans.
So-called word-based, proportional analogies of the form (A : B :: C : D)
(Mikolov et al., 2013a,b; Gladkova et al., 2016; Ushio et al., 2021) are
often used to measure the potential of word embeddings and language models.
Recent studies (Webb et al., 2023) show a strong ability of
state-of-the-art (SOTA) large language models (LLMs) to discover
proportional word analogies, though this skill degrades with higher
complexity (Wijesiriwardene et al., 2023) or when controlling for
association-based answers (Stevenson et al., 2023). Shifting toward more
complex settings, narrative-based analogy benchmarks that involve system
mappings rather than simple word-based relational mappings have been also
been considered recently, with limitations in scope, generalizability, and
alignment with cognitive theories (Nagarajah et al., 2022; Wijesiriwardene
et al., 2023; Sourati et al., 2023). Meanwhile, given the potential of
large language and visual models, another line of research aims to study
their ability to draw analogies consistently (cf., Webb et al., 2023).
Given the richness of analogical abstraction and the wide interest in this
topic from artificial intelligence, linguistics, and cognitive psychology,
it is important to connect these communities and facilitate
cross-disciplinary activities.