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.

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 
[email protected].

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/

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/

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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