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NLRSE 2023 : Natural Language Reasoning and Structured Explanations Workshop at ACL 2023

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Link: https://nl-reasoning-workshop.github.io/
 
When Jul 13, 2023 - Jul 13, 2023
Where ACL 2023 (Toronto, Canada)
Submission Deadline Apr 24, 2023
Notification Due May 22, 2023
Final Version Due May 30, 2023
Categories    natural language processing   NLP   reasoning   explanations
 

Call For Papers

***Second Call for Papers***
*** Natural Language Reasoning and Structured Explanations Workshop at ACL 2023 ***
Website: https://nl-reasoning-workshop.github.io/ [1]

Dates
-----------
All deadlines are 11:59 PM AoE time.
- Workshop Paper Due Date: April 24, 2023
- Notification of acceptance: May 22, 2023
- Camera-ready papers due:May 30, 2023
- Workshop date: TBD
(ACL 2023 will take place in Toronto, Canada from 9th to 14th July, 2023.)

List of Topics
------------------

We welcome submissions on all topics related to natural language reasoning
or structured explanations, which might include:
- Multi-step natural language reasoning
- Structured explanations
- Foundations of natural language reasoning
- Applications of natural language reasoning
- Knowledge retrieval for multi-step reasoning
- Reasoning as programs

With recent scaling of large pre-trained Transformer language models (LLMs),
the scope of feasible NLP tasks has broadened, including tasks requiring
increasingly complex reasoning. Although LLMs have shown remarkable
performance, it is still unclear how to best elicit this reasoning and how
the answers that models give follow from what they "know." This
workshop aims to bring together a diverse set of perspectives and attempt to
establish common ground for how various kinds of explanation structures can
tackle a broad class of reasoning problems in natural language and beyond.
As such, the workshop welcomes and covers a wide range of topics, including
(non-exclusively):

**Multi-step natural language reasoning:** Solving reasoning problems, such
as those involving abstract manipulations, has been a long-standing
challenge in the field of artificial intelligence. Large language models
have recently achieved a new state-of-the-art performance on many reasoning
benchmarks, often with approaches only requiring prompting. Current research
frontiers are exploring what kinds of explanation formats are most
effective, how reasoning is most effectively broken down, how to get
language models to plan their reasoning, and what resources can be used to
improve reasoning capabilities of language models. Tasks include
mathematical reasoning, logical reasoning, commonsense reasoning, and more.

**Structured explanations:** Explanations for these complex tasks are
typically composed of two or more facts that are used to help the reasoning
process while also providing a record of the path taken to arrive at an
inference. What representations can be best used by inference algorithms to
construct large explanations? Frontiers of research include exploring search
algorithms over such representations, how to represent annotations at scale
and continual learning models.

**Foundations of natural language reasoning:** Does the structured reasoning
constitute a plausible (interpretable to humans) and faithful (true to the
model's processes) explanation? Does perturbing the reasoning lead to
correctly modified behavior?
Applications of natural language reasoning: New QA settings, language
grounding, explainable diagnosis systems, theorem provers using natural
language, reasoning for scientific discovery, and more.

**Knowledge retrieval for multi-step reasoning:** It has been shown that
LLMs can store factual knowledge implicitly in their parameters, however,
their ability to access and manipulate knowledge is still limited. Future
avenues of research include effective methods to combine parametric and
non-parametric knowledge for complex reasoning, conditioning retrieval
given intermediate reasoning context, retrieving better provenance for
structured explanations.

**Reasoning as programs:** Another body of work within computational
cognitive science and AI has formalized reasoning as inference over
programs, building on classical views of human reasoning in a symbol-like
language of thought and linguistic semantics with logical languages.
Language models of code to produce structured reasoning for commonsense
problems or other similar approaches are all in scope here

Submission Guidelines
--------------------------------
We welcome two types of papers: regular workshop papers and non-archival
submissions. Only regular workshop papers will be included in the workshop
proceedings. All submissions should be in PDF format and made through the
Softconf website set up for this workshop
(https://softconf.com/acl2023/nl-reasoning/ [2]). In line with the ACL main
conference policy, camera-ready versions of papers will be given one
additional page of content.

**Regular workshop papers:** Authors should submit a paper up to 8 pages
(both short and long papers are welcome), with unlimited pages for
references, following the ACL 2023 formatting requirements. The reported
research should be substantially original. All submissions will be reviewed
in a single track, regardless of length. Accepted papers will be presented
as posters by default, and best papers may be given the opportunity for a
brief talk to introduce their work. Reviewing will be double-blind, and thus
no author information should be included in the papers; self-reference that
identifies the authors should be avoided or anonymised. Accepted papers will
appear in the workshop proceedings.

**Non-archival submissions:** We also solicit cross-submissions, i.e.,
papers on relevant topics that have appeared in other venues (e.g., workshop
or conference papers at NLP, ML, or cognitive science venues, among others).
Accepted papers will be presented at the workshop, with an indication of
original venue, but will not be included in the workshop proceedings.
Cross-submissions are ideal for related work which would benefit from
exposure to the NLReasoning audience. Interested authors should submit their
papers in PDF format through the NLReasoning Softconf website, with a note
on the original venue. They will be reviewed in a single-blind fashion.
Papers in this category do not need to follow the ACL format, and the
submission length is determined by the original venue. The paper selection
will be solely determined by the organizing committee.

In addition, we welcome papers on relevant topics that are under review or
to be submitted to other venues (including the ACL 2023 main conference).
These papers must follow the regular workshop paper format and will not be
included in the workshop proceedings. Papers in this category will be
reviewed by workshop reviewers.

Note to authors: While you submit your paper through Softconf
(https://softconf.com/acl2023/nl-reasoning/ [3]), please select the
"Submission Type" properly based on the guidelines.

For questions about the submission guidelines, please contact workshop
organizers via nl-reasoning@googlegroups.com [4].

Organizers
----------------

Bhavana Dalvi, Allen Institute for AI
Greg Durrett, UT Austin
Peter Jansen, University of Arizona
Danilo Ribeiro, Northwestern University
Catherine Wong, Massachusetts Institute of Technology
Jason Wei, Google Brain

Read more:
https://www.aclweb.org/portal/content/natural-language-reasoning-and-structured-explanations-workshop-planned-workshop-eacl-acl

[1] https://nl-reasoning-workshop.github.io/
[2] https://softconf.com/acl2023/nl-reasoning/
[3] https://softconf.com/acl2023/nl-reasoning/
[4] mailto:nl-reasoning@googlegroups.com

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