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 medexact-acl2026+owner@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/

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.