CFP: BIONLP 2024 and Shared Tasks @ ACL 2024


The 23rd BioNLP workshop associated with the ACL SIGBIOMED special interest group is co-located with ACL 2024 in Bangkok, Thailand


Paper submission deadline: May 17 (Friday), 2024
Notification of acceptance: June 17 (Monday), 2024
Camera-ready paper due: July 1 (Monday), 2024
Workshop: August 16, 2024, Location: LOTUS SUITE 12


Two types of submissions are invited: full (long) papers (8 pages) and short papers (4 pages).

Submission site for the workshop

Please follow these formatting guidelines:

Final versions of accepted papers will be given one additional page of content (up to 9 pages for long papers, up to 5 pages for short papers) to address reviewer comments.


Titipat Achakulvisut. Biomedical and Data (Bio-Data) lab at Mahidol University


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.

BioNLP 2024 will be particularly interested in transparency of the generative approaches and factuality of the generated text. Language processing that supports DEIA (Diversity, Equity, Inclusion and Accessibility) is still of utmost importance. The work on detection and mitigation of bias and misinformation continues to be of interest. Research in languages other than English, particularly, under-represented languages, and health disparities are always of interest to BioNLP. Other active areas of research include, but are not limited to:

  • Tangible results of biomedical language processing applications;
  • Entity identification and normalization (linking) for a broad range of semantic categories;
  • Extraction of complex relations and events;
  • Discourse analysis; Anaphora & coreference resolution;
  • Text mining & Literature based discovery;
  • Summarization;
  • Text simplification;
  • Question Answering;
  • Resources and strategies for system testing and evaluation;
  • Infrastructures and pre-trained language models for biomedical NLP;
  • Processing and annotation platforms;
  • Synthetic data generation & data augmentation;
  • Translating NLP research into practice;
  • Getting reproducible results.


1. Clinical Text generation

Task 1: Radiology Report Generation

An important medical application of natural language generation (NLG) is to build assistive systems that take X-ray images of a patient and generate a textual report describing clinical observations in the images. This is a clinically important task, offering the potential to reduce radiologists repetitive work and generally improve clinical communication. This shared task is using the first large-scale collection of RRG datasets based on MIMIC-CXR, CheXpert, PadChest and CANDID-PTX. Participants will need to generate findings and impression from chest x-rays and will be evaluated on a common leaderboard with recent proposed metrics such as F1-Radgraph and RadCliQ. This shared task aims to benchmark recent progress using common data splits and evaluation implementations.

See details at

Task 2: Discharge Me!

The primary objective of this task is to reduce the time and effort clinicians spend on writing detailed notes in the electronic health record (EHR). Clinicians play a crucial role in documenting patient progress in discharge summaries, but the creation of concise yet comprehensive hospital course summaries and discharge instructions often demands a significant amount of time, especially since these sections cannot be readily copied from prior notes. This can lead to clinician burnout and operational inefficiencies within hospital workflows. By streamlining the generation of these sections, we can not only enhance the accuracy and completeness of clinical documentation but also significantly reduce the time clinicians spend on administrative tasks, ultimately improving patient care quality.

See details at

2. BioLaySumm

This shared task surrounds the abstractive summarization of biomedical articles, with an emphasis on catering to non-expert audiences through the generation of summaries that are more readable, containing more background information and less technical terminology (i.e., a "lay summary").

This is the 2nd iteration of BioLaySumm, following the success of the 1st edition of the task at BioNLP 2023 which attracted 56 submissions across 20 different teams. In this edition, we aim to build on the task from last year by introducing a new test set, updating our evaluation protocol, and encouraging participants to explore novel approaches that will help to further advance the state-of-the-art for Lay Summarization.

See details at


  • Dina Demner-Fushman, US National Library of Medicine
  • Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK
  • Makoto Miwa, Toyota Technological Institute, Japan
  • Kirk Roberts, UTHealth, Houston, Texas
  • Jun-ichi Tsujii, National Institute of Advanced Industrial Science and Technology, Japan

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