37.181, Confs: 64th Annual Meeting of the Association for Computational Linguistics: Industry Track (USA)

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LINGUIST List: Vol-37-181. Thu Jan 15 2026. ISSN: 1069 - 4875.

Subject: 37.181, Confs: 64th Annual Meeting of the Association for Computational Linguistics: Industry Track (USA)

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Date: 13-Jan-2026
From: ACL Announcements [announcements at aclweb.org]
Subject: 64th Annual Meeting of the Association for Computational Linguistics: Industry Track


64th Annual Meeting of the Association for Computational Linguistics:
Industry Track
Short Title: ACL
Theme: ACL 2026 Industry Track

Date: 02-Jul-2026 - 07-Jul-2026
Location: San Diego, USA
Meeting URL: https://2026.aclweb.org/

Linguistic Field(s): Applied Linguistics; Computational Linguistics

Submission Deadline: 14-Feb-2026

ACL 2026 Industry Track in San Diego, CA, United States
Conference: July 2 - 7, 2026
Paper submission deadline: February 14, 2026
Background:
Language technologies are an integral and critical part of our daily
lives. Many of these applications have their roots in academic and
industrial research laboratories where researchers invented a plethora
of algorithms, benchmarked them against shared datasets and perfected
their performance to provide plausible solutions to real-world
applications. While a controlled laboratory setting is vital for a
deeper scientific understanding of the problems underlying language
technologies and the impact of algorithmic design choices on their
performance, transitioning the technology to real-world industrial
strength applications raises a different, yet challenging, set of
technical issues.
We acknowledge the challenges when adapting language technologies for
building novel and robust real-world applications as the journey from
theoretical research to practical deployment can be difficult.
Challenges can include technical aspects of system deployment and
optimizing for efficiency, making informed design choices or
methodological considerations of incorporating human feedback,
evaluation and oversight. To provide a forum to address these
multifaceted issues, we are seeking submissions that not only dive
into research but also demonstrate the application of systems in
real-world scenarios, irrespective of whether they involve proprietary
data.
Topics:
We invite submissions describing innovations and implementations in
all areas of speech and natural language processing (NLP) technologies
and systems that are relevant to real-word applications. The primary
focus of this track is on papers that advance the understanding of,
and demonstrate the effective handling of, practical issues related to
the deployment of language processing or language generation
technologies, including those of large language models, in non-trivial
real-world systems, meaning: applications deployed for real-world use,
i.e., outside controlled environments such as laboratories, classrooms
or experimental crowd-sourced setups, also including applications that
use NLP and/or speech technology, even if not state of the art in
terms of research. There is no requirement that the system be made by
a for-profit company, but the users of the system are most likely
outside the NLP research community.
This track provides an opportunity to highlight the key insights and
new research challenges that arise from real world implementations.
Relevant areas include:
A. System design, efficiency, maintainability and scalability of
real-world applications, with topics in alphabetical order including,
but not limited to:
 - Benchmarks and methods for improving the latency and efficiency of
systems
 - Continuous maintenance and improvement of deployed systems
 - Efficient methods for training and inference
 - Enabling infrastructure for large-scale deployment
 - Handling unexpected user behaviour
 - Human-in-the-Loop approaches to application development
 - Implementation at speed, scale and low-cost
 - Negative results related to real-world applications
 - System combination
B. Novel applications and use cases, with topics in alphabetical order
including, but not limited to:
 - Best practices and lessons learned
 - Case studies, from design to deployment
 - Description of an application or system
 - Design of application-relevant datasets
 - Development of methods under system constraints (model or data
size)
 - Novel, previously unsolved NLP problems and novel NLP applications
C. Methods for deployed systems, with topics in alphabetical order
including, but not limited to:
 - Ethics, bias, fairness, harmlessness and trustworthiness in
deployed systems
 - Interpretability
 - Interactive systems
 - Offline and online system evaluation methodologies
 - Online learning
 - Robustness
In addition, opinion/vision papers related to real-world applications
are also welcome.
Submissions must clearly identify one of the following three areas
they fall into:
1. Deployed: Must describe a system that solves a non-trivial
real-world problem. The focus may include describing the problem
related to actual use cases, its significance (against opportunity
size, value proposition, and ideal end state), design/formulation of
methods, tradeoff design decision for solutions, deployment
challenges, and lessons learned.
2. Emerging: Must describe the development of a system that solves a
non-trivial real-world problem (it need not be deployed or even close,
but there needs to be evidence that this development is intended for
real-world deployment). Papers that describe enabling infrastructure
for large-scale deployment of NLP techniques also fall in this
category.
3. Discovery: Must include results obtained from NLP applications in
real-world scenarios that result in actionable insights. These
discoveries should reveal promising directions in their application
areas, leading to further system or societal enhancements. For
example, an actionable discovery from an analysis of call center
transcripts may reveal that certain language choices negatively impact
customer experience, leading to better training of service
representatives and improved customer experience.
Important Dates:
Paper submission deadline: February 14, 2026
Author response deadline: March 29, 2026
Notification of acceptance: April 12, 2026
Camera-ready deadline: April 19, 2026
Main conference: July 2-7, 2026
All deadlines are 11.59 pm UTC -12h (anywhere on earth).
Following the ACL and ARR Policies for Review and Citation, updated in
early 2024, there is no anonymity period requirement, e.g., one may
upload the paper to arXiv at any time.
Please note that the ACL 2026 Industry Track does not use ARR!
Evaluation and Decision Criteria:
Submissions will be reviewed in a double-blind manner and assessed
based on their novelty, technical quality, potential impact, and
clarity. Submissions to the industry track should emphasize real-world
implementations of NLP systems, the development of such systems, or
provide insights based on real-world datasets with obvious industry
impact. For papers that rely heavily on empirical evaluations, the
experimental methods and results should be clear, well executed, and
reproducible (though the data may be proprietary); in that regard, due
to the type of work we expect to be submitted to the Industry Track,
we ask authors to pay specific attention to their evaluation
methodologies (human vs. automated).
Submission Requirements:
Authors are invited to submit original, full-length (6 pages) industry
track papers that are not previously published, accepted to be
published, or under consideration for publication in any other forum.
Manuscripts should be submitted digitally, in PDF format and formatted
using the ACL 2026 formatting requirements. Please do not modify these
style files, nor should you use templates designed for other
conferences. Submissions that do not conform to the required styles,
including paper size, margin width, and font size restrictions, will
be desk-rejected.
Length and appendices: Industry Track papers cannot exceed 6 pages in
length (excluding ethical considerations and references). After the
bibliography, papers can have an optional appendix with, e.g.,
examples or sample inputs/outputs, pre-processing decisions, model
parameters, feature templates, pseudocode, information about user
studies, additional errors analysis or other details that are
necessary for the replication of the work described in the paper.
Note, however, that paper submissions must be fully self-contained,
i.e., supplementary materials, as provided in the appendix, are
completely optional, and reviewers are not even asked to review them.
Note that it will not be possible to submit additional separate files
as supplementary materials. Authors are asked not to abuse the option
of an unlimited appendix and only to include material that supports
the primary messages and content of the paper; to avoid any
misunderstandings regarding the nature of the appendix, for the final
papers, especially those with an appendix of excessive length, the ACL
2026 Industry Track chairs reserve the right to include a statement
that it was not mandatory for reviewers to review the material
presented in the appendix.
Double-blind review: Industry Track submissions must neither include
the authors' names nor their affiliations. Self-references that reveal
the authors' identities must be avoided. For example, instead of "We
previously showed (Smith, 1991) …" or even "We previously showed
(Anonymous, 1991) …", please use "Smith (1991) previously showed …".
Authors should also be careful not to reveal their affiliation
indirectly, for example through screenshots or trade names.
Submissions should avoid links to non-anonymized repositories: code
should be submitted as a link to an anonymized repository (e.g.,
Anonymous GitHub or Anonym Share). Please avoid links to storage
services like Dropbox (which may track the reviewers downloading the
resources). Papers that do not conform to these requirements will be
desk-rejected.
Citation and comparison: Authors are expected to cite all refereed
publications relevant to their submission but may be excused for not
knowing about all unpublished work (especially work that has been
recently posted and/or is not widely cited). In cases where a preprint
has been superseded by a refereed publication, the refereed
publication should be cited in addition to or instead of the preprint
version. Papers (whether refereed or not) appearing less than 3 months
before the submission deadline are considered contemporaneous to a
submission, and authors are therefore not obliged to make detailed
comparisons that require additional experimentation and/or in-depth
analysis. For more information, see the ACL Policies for Review and
Citation.
Writing assistance: The ACL 2026 Industry Track adheres to the ACL
policy on using writing assistants (including AI-based writing
assistants and other AI tools) available here.
Submission system: Papers have to be submitted through the ACL 2026
Industry Track online submission system. The submission link will be
provided soon.
Final version: Accepted papers will be given one additional page of
content (up to 7 pages; ethical considerations, acknowledgements and
references do not count against this limit) so that reviewers'
comments can be taken into account. Previous presentations of the work
(e.g., preprints on arXiv.org) should be indicated in a footnote that
should be excluded from the review submission, but included in the
final version of papers appearing in the ACL 2026 proceedings.
The final version should remove anonymization in text, citation, and
figures. For example, the final version may include the name of the
authors' institutions, trademarks, and screenshots of identifiable
products. Please notice that once the paper has been submitted, no
changes to the list of authors are allowed.
Presentation requirement for accepted papers: Industry Track papers
will be presented orally or as posters, to be determined by the
program committee. All accepted papers must be presented at the
conference (either via online or onsite presence). At least one author
of each accepted paper must register for ACL 2026 by the early
registration deadline. The ACL 2026 Industry Track will run in
parallel with the Research Track.
Presentation Mode: Accepted papers will be presented orally or as
posters as determined by the program committee. The decisions as to
which papers will be presented orally and which as poster
presentations will be based on the nature rather than the quality of
the work. There will be no distinction in the proceedings between
papers presented orally or as posters
Authorship: The author list for submissions should include all (and
only) individuals who made substantial contributions to the work
presented. Each author listed on a submission to the ACL 2026 Industry
Track will be notified of submissions and the final decision. No
changes to the order or composition of authorship may be made to
submissions to the ACL 2026 Industry Track after the paper submission
deadline
Multiple Submission Policy:
ACL 2026 will not consider any paper that is under review in a journal
or another conference at the time of submission, and submitted papers
must not be submitted elsewhere during the ACL 2026 review period.
This policy covers all refereed and archival conferences and workshops
(e.g., NeurIPS, ACL workshops), as well as ARR. In addition, we will
not consider any paper that overlaps significantly in content or
results with papers that have been (or will be) published elsewhere.
Authors submitting more than one paper to ACL 2026 must ensure that
their submissions do not overlap significantly (>25%) with each other
in content or results.
Submissions of identical or closely related work to multiple ACL 2026
tracks (e.g., to the research track and industry track) will be
treated as duplicate submissions. Such submissions violate our
multiple submission policy and will be rejected without review. The
authors should also include the papers that their paper overlaps with
or extends in the references section as follows: _Anonymous Authors_,
"_Title of the paper_", _Under submission at ACL 2026 (TRACK NAME)_.
Ethics Policy:
Authors are required to honor the ethical code set out in the ACL Code
of Ethics. The consideration of the ethical impact of our research,
use of data, and potential applications of our work has always been an
important consideration, and as artificial intelligence is becoming
more mainstream, these issues are increasingly pertinent. We ask that
all authors read the code, and ensure that their work is conformant to
this code. Where a paper may raise ethical issues, we ask that you
include in the paper an explicit discussion of these issues, which
will be taken into account in the review process. We reserve the right
to reject papers on ethical grounds, where the authors are judged to
have operated counter to the code of ethics, or have inadequately
addressed legitimate ethical concerns with their work.
Authors will be allowed extra space after the sixth page for an
optional broader impact statement or other discussion of ethics. The
ACL review form will include a section addressing these issues and
papers flagged for ethical concerns by reviewers or ACs will be
further reviewed by an ethics committee. Note that an ethical
considerations section is not required, but papers working with
sensitive data or on sensitive tasks that do not discuss these issues
will not be accepted. Conversely, the mere inclusion of an ethical
considerations section does not guarantee acceptance. In addition to
acceptance or rejection, papers may receive a conditional acceptance
recommendation. Camera-ready versions of papers designated as
conditional accept will be re-reviewed by the ethics committee to
determine whether the concerns have been adequately addressed. Please
read the ethics FAQ for more guidance on some problems to look out for
and key concerns to consider relative to the code of ethics.
Contact Information:
Industry Track Co-Chairs:
Yunyao Li (Adobe)
Georg Rehm (DFKI GmbH)
Mei Tu (Samsung)
Email: acl-2026-industry-track at googlegroups.com
General Chair: Philipp Koehn (Johns Hopkins University)



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