37.3424, Confs: 1st Workshop on Linguistic Information for Downstream Applications at EACL 2027 (Greece)

Tue Oct 20 11:05:02 UTC 2026

LINGUIST List: Vol-37-3424. Tue 20 Oct 2026. ISSN: 1069 - 4875.

Subject: 37.3424, Confs: 1st Workshop on Linguistic Information for Downstream Applications at EACL 2027 (Greece)

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Date: 16-Oct-2026
From: Marion Di Marco [marion.dimarco at tum.de]
Subject: 1st Workshop on Linguistic Information for Downstream Applications at EACL 2027

1st Workshop on Linguistic Information for Downstream Applications at
EACL 2027
Short Title: LInDA 2027

Date: 13-Mar-2027 - 14-Mar-2027
Location: Athens, Greece
Contact: Marion Di Marco
Contact Email: linda2027 at dlsi.ua.es
Meeting URL: https://linda-workshop.github.io/

Linguistic Field(s): Applied Linguistics; Computational Linguistics;
Lexicography; Morphology; Translation

Submission Deadline: 15-Dec-2026

Co-located with EACL 2027 in Athens, Greece (March 13 or 14, 2027)
Many languages remain underrepresented in large language models
(LLMs), resulting in limited performance on downstream tasks. However,
many languages have other resources: dictionaries, grammars,
morphological analyses, and other forms of annotated texts. According
to Glottolog, about 63% of the world's languages have at least a
grammar sketch.
Linguistic annotations and resources have gained attention in the NLP
community as a means of providing auxiliary information for various
NLP tasks, such as machine translation.  Linguistic information can
help models to better process and represent languages, particularly in
low-resource settings where conventional training data are scarce.
LInDA brings these two sides together: linguistic information and NLP
applications. We want to understand how linguistic resources and
annotations can improve downstream tasks, especially for low-resource
and underrepresented languages. We also aim to know which types of
information are useful, and for which tasks.
The workshop has two goals:
    - To evaluate strategies that integrate linguistic information
into language models, and to measure their impact on specific
applications.
    - To explore how language models can help create or extract
linguistic information.
We welcome researchers with a background in linguistics, in computer
science, or in both.
LInDA’s objective is to assess approaches on how languages that lack
conventional training data can benefit from linguistic information,
particularly for underrepresented languages. The workshop’s central
focus is the connection between linguistic annotation and NLP tasks.
LInDA invites papers on the use of linguistic information to improve
NLP applications, such as machine translation, information retrieval,
interlinear gloss generation, morphological analysis, parsing,
grammatical annotation, and training of language-specific models,
among others.
Importantly, each submission must address two points: (1) a linguistic
resource or annotation (not necessarily created by the contributors),
and (2) its impact on one or more NLP tasks. Papers that cover only
one of these points are out of scope.
Topics include, but are not limited to:
   - Leveraging linguistic resources (e.g., annotations, grammars) to
improve NLP tasks, particularly for low-resource languages
   - Language- and task-specific case studies, as well as more general
strategies
   - Practical NLP applications, such as tools for second language
(L2) learners and for educational purposes, based on linguistically
informed material
   - Analyzing the quality of linguistic resources (notably,
annotation standardization) and their relevance in NLP downstream
tasks
   - Assessing how linguistic knowledge is represented in pretrained
models as a basis for improving their capabilities
   - Investigating the language proficiency and metalinguistic skills
of language models
   - Ethics of data collection, annotation, and NLP tools for
underrepresented languages
   - Data- and compute-efficient approaches for multi- and
mono-lingual modeling, relying on linguistic information for
improvement
   - Cross-lingual transfer by utilizing linguistic knowledge that
supports generalization
We note that some papers might deviate from the dual requirements
outlined above, for example, position papers and papers discussing the
ethical considerations surrounding data collection, annotation, and
NLP tool development for underrepresented languages.
Paper Types:
Archival papers.
  -  Long papers: up to 8 pages of content. They should describe
original, completed, and unpublished work. It should include
evaluation and analysis where possible.
  -  Short papers: up to 4 pages of content. They should present a
focused contribution, such as a small experiment, a negative result, a
new resource, or an application.
Non-archival papers.
Accepted papers will be presented at the workshop but will not appear
in the proceedings. This track is open to extended abstracts (up to 2
pages) on ongoing work, and to work already published elsewhere.
Submission Format:
Submissions must follow the two-column ACL format. Please use the
official ACL LaTex template. Submissions must be in PDF. All papers
must include a Limitations section after the conclusion. This section
does not count toward the page limit. An Ethical Considerations
section is optional and also does not count toward the limit.
Important Dates
Direct submission deadline: December 15, 2026
Pre-reviewed ARR commitment deadline: December 22, 2026
Notification of Acceptance: TBA (January 2027)
Camera-ready deadline: January 19, 2027
Co-located with EACL 2027 in Athens, Greece (March 13 or 14, 2027)
Website: https://linda-workshop.github.io/
Contact: linda2027 at dlsi.ua.es
Organizing Committee
-    Marion Di Marco, Technical University of Munich, Germany
-    Niyati Bafna, Johns Hopkins University, USA
-    Aleksei Dorkin, University of Tartu, Estonia
-    Shu Okabe, CentraleSupélec, Université Paris-Saclay, France
-    Juan Antonio Pérez-Ortiz, Universitat d'Alacant, Spain
-    Felipe Sánchez-Martínez, Universitat d'Alacant, Spain
-    Abishek Stephen, Charles University, Czech Republic
-    Tsedeniya Kinfe Temesgen, Technical University of Munich, Germany
-    Alexander Fraser, Technical University of Munich, Germany

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