37.799, Confs: BioNLP Workshop and Shared Tasks @ ACL 2026 (USA)

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LINGUIST List: Vol-37-799. Fri Feb 27 2026. ISSN: 1069 - 4875.

Subject: 37.799, Confs: BioNLP Workshop and Shared Tasks @ ACL 2026 (USA)

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Date: 24-Feb-2026
From: Mohamed Elgaar [mohdelgaar at gmail.com]
Subject: BioNLP Workshop and Shared Tasks @ ACL 2026


BioNLP Workshop and Shared Tasks @ ACL 2026

Date: 03-Jul-2026 -
Location: San Diego, USA
Contact: Dina Demner-Fushman
Contact Email: ddemner at mail.nih.gov
Meeting URL: https://aclweb.org/aclwiki/BioNLP_Workshop

Linguistic Field(s): Applied Linguistics; Clinical Linguistics;
Computational Linguistics; General Linguistics

Submission Deadline: 14-Apr-2026

BIONLP 2026 and Shared Tasks @ ACL 2026
https://aclweb.org/aclwiki/BioNLP_Workshop
Tentative Important Dates:
(All submission deadlines are 11:59 p.m. UTC-12:00 “anywhere on
Earth”)
Paper submission deadline: April 17 (Friday), 2026
Notification of acceptance: May 4 (Monday), 2026
Camera-ready paper due: May 12 (Tuesday), 2026
Workshop: July 3 OR 4, 2026
Please watch for the updates!
Submission Instructions:
Two types of submissions are invited: full papers and short papers.
Full papers should not exceed eight (8) pages of text, plus unlimited
references. These are intended to be reports of original research.
BioNLP aims to be the forum for interesting, innovative, and promising
work involving biomedicine and language technology, whether or not
yielding high performance at the moment. This by no means precludes
our interest in and preference for mature results, strong performance,
and thorough evaluation.  Both types of research and combinations
thereof are encouraged.
Short papers may consist of up to four (4) pages of content, plus
unlimited references. Appropriate short paper topics include
preliminary results, application notes, descriptions of work in
progress, etc.
Electronic Submission:
Submissions must be electronic and in PDF format, using the Softconf
START conference management system
Submissions need to be anonymous.
Submission site for the workshop: START system (link coming soon)
Please follow the ACL formatting guidelines:
https://github.com/acl-org/acl-style-files
Dual submission policy: papers may NOT be submitted to  the BioNLP
workshop if they are or will be concurrently submitted to another
meeting or publication.
Workshop Overview and Scope:
The BioNLP workshop, associated with the ACL SIGBIOMED special
interest group, is an established primary venue for presenting
research in language processing and language understanding for the
biological and medical domains. The workshop has been running every
year since 2002 and continues getting stronger. Many other emerging
biomedical and clinical language processing workshops can afford to be
more specialized because BioNLP truly encompasses the breadth of the
domain and brings together researchers in bio- and clinical NLP from
all over the world.
The interest in biomedical and clinical language continues to  broaden
due to unprecedented advances supported by success stories in
improving health through supporting patients and clinicians. Access to
biomedical information became easier, and more people generate and
access health-related text. Only language technologies can enable and
support adequate use of the biomedical and clinical text in most use
cases.
The advances in pre-trained language models and foundation models make
all parties involved in healthcare turn to language technologies in
the hope of getting tangible support in satisfying information needs,
facilitating research and improving clinical documentation and
healthcare.
In addition to exposing BioNLP researchers to the mainstream ACL
research, the workshop is a venue for informing the mainstream ACL
researchers about the fast growing and important domain of biomedical
/ clinical language processing.
BioNLP 2026 will focus on evaluation frameworks and metrics that
reflect the needs of health-related use cases and provide a good
estimate of reliability of the proposed solutions. BioNLP 2026 will
continue focusing on transparency of the generative approaches and
factuality of the generated text. Language processing that supports
DEIA (Diversity, Equity, Inclusion and Accessibility) continues to be
of utmost importance. The work on detection and mitigation of bias and
misinformation continues to be paramount. Research in languages other
than English, particularly, under-represented languages, and health
disparities are always of interest to BioNLP. Other areas of interest
include, but are not limited to:
 - Entity identification and normalization (linking) for a broad range
of semantic categories;
 - Extraction of complex relations and events;
 - Discourse analysis; Anaphora / coreference resolution;
 - Question Answering; Summarization; Text simplification;
 - Resources and strategies for system testing and evaluation;
 - Synthetic data generation and data augmentation;
 - Translating NLP research into practice: tangible explainable
results of biomedical language processing applications.
 - Reproducibility of the published findings.
Shared Tasks:
BioNLP has a long-standing tradition of sponsoring Shared Tasks. This
year, we invited SIGBioMed members to submit a description of a shared
task to be included with the BioNLP proposal. We received four strong
detailed descriptions of the tasks, which were reviewed by the
workshop organizers. These well-defined and timely tasks are briefly
described below.
1. MedExACT:
This task involves detection and labeling of medical decisions in ICU
discharge summaries, with evaluation metrics emphasizing both accuracy
and fairness across demographic and disease subgroups at the span and
token levels, as well as through stratified analyses to measure
robustness against biases in sex, race, English proficiency, and
disease type. Baseline models such as RoBERTa indicated the complexity
of the task, and participants will be supported with expedited access
to MedDec through PhysioNet, a public leaderboard, and a starter kit
in Python. The training and validation splits of MedDec are currently
available on PhysioNet, while the test split has not been released and
will remain withheld until the evaluation phase.
Please join the google group to receive notifications and register
your team https://groups.google.com/g/medexact-acl2026.
If you have any question, feel free to send an email to
medexact-acl2026+owner at googlegroups.com.
PsyDefDetect: Detecting Psychological Defense Mechanisms in
Conversations
This task focuses on classifying Seeker’s utterances in supportive
conversations into specific Psychological Defense Levels based on the
Defense Mechanism Rating Scales (DMRS) framework. The benchmark
addresses the challenge of capturing subtle linguistic cues of
deep-seated psychological mechanisms within highly informal and
context-dependent emotional dialogues. This initiative supports
research at the intersection of clinical psychology and NLP, aiming to
operationalize complex psychological constructs for computational
analysis. Participating systems will be ranked using Accuracy,
Precision, Recall, and F1-score.
Task Homepage: https://psydefdetect-shared-task.github.io/
2. MedGenVidQA:
The MedGenVidQA shared task focuses on developing systems that utilize
generative models to retrieve relevant multimodal (textual and visual)
sources and to localize visual answers within medical videos in
response to consumer and healthcare professional medical queries.
Additionally, resource creation in the medical domain is both costly
and time-consuming, as it often requires medical expertise. In this
context, we also aim to assess the capability of generative models to
create question–answer pairs from medical videos. Following earlier
editions of medical question answering tasks: MedVidQA 2023, MedVidQA
2024, BioGen 2024, and BioGen 2025, this shared task expands medical
video question answering for both professionals and consumers, with a
focus on generative approaches to solving these tasks.
See details at https://medgenvidqa.github.io/
3. Clinical Skill QA:
This task extends evaluation to a multimodal setting. Given an image
of a medical student’s procedure, a question, and four answer options,
the goal is for participants to train a model to generate the correct
response. The dataset will be constructed from ~80 video clips of
medical student clinical procedures, collected from a partner medical
school. This task provides a unified framework for benchmarking,
diagnosing, and advancing LLM capabilities for both clinical decision
support and medical training. Evaluation will follow a multiple-choice
QA setup with accuracy as the primary metric, with additional
stratified analyses by skill type and modality.



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