BioNLP Workshop
WORKSHOP TOPIC AND CONTEXT
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:
- 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 \& data augmentation;
- Translating NLP research into practice: tangible explainable results of biomedical language processing applications;
- Reproducibility of the published findings.