BioNLP Workshop
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
IMPORTANT DATES
TENTATIVE
- Paper submission deadline: May 17 (Friday), 2024
- Notification of acceptance: June 24 (Tuesday), 2024
- Camera-ready paper due: July 5 (Friday), 2024
- Workshop: August 16, 2024, Location: TBA
SUBMISSION INSTRUCTIONS
Two types of submissions are invited: full (long) papers (8 pages) and short papers (4 pages).
Submission site for the workshop https://softconf.com/acl2024/BioNLP2024
Please follow these formatting guidelines: https://github.com/acl-org/acl-style-files
Please note that the review process is double-blind.
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 reviewers’ comments.
Submissions from ACL rolling review
We will accept ACL rolling review submissions with all reviews and scores. If you are interested in submitting your work for consideration, please contact ddemner at gmail.
INVITED TALK
Speaker: Titipat Achakulvisut, Department of Biomedical Engineering, Mahidol University, Thailand
Biomedical and Data (Bio-Data) lab at Mahidol University
Title: Enhancing Neuroscience Conferences through Natural Language Processing
Abstract: This talk presents the development and implementation of natural language processing (NLP) tools at neuroscience conferences. We have successfully integrated these tools into various conferences, including a recommendation engine at the Society for Neuroscience (SfN) meeting, one-on-one matching at the Conference on Cognitive Computational Neuroscience (CCN), paper-reviewer matching for the Computational and Systems Neuroscience (COSYNE) conference, and reviewer recommendations for NBDT journal. We employ a fine-tuning and contrastive learning approach to adapt transformer-based models, such as MiREAD and SciBERT for neuroscience. We evaluate these models using both distance metrics and recommendation arena assessments. In sum, we explore NLP tools in non-computer science domains, aiming to enhance the interactions of researchers and attendees.
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.
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 https://stanford-aimi.github.io/RRG24/
- 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 https://stanford-aimi.github.io/discharge-me/
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 last year’s task 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 https://biolaysumm.org/
Organizers
* 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
Program Committee
* Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK * Emilia Apostolova, Anthem, Inc., USA * Eiji Aramaki, University of Tokyo, Japan * Leonardo Campillos-Llanos, Centro Superior de Investigaciones Científicas - CSIC, Spain * Mike Conway, University of Melbourne, Australia * Surabhi Datta, Melax Technologies, USA * Berry de Bruijn, National Research Council, Canada * Dina Demner-Fushman, US National Library of Medicine * Dmitriy Dligach, Loyola University Chicago, USA * Kathleen C. Fraser, National Research Council Canada * Yanjun Gao, University of Wisconsin-Madison, USA * Natalia Grabar, CNRS, U Lille, France * Cyril Grouin, Université Paris-Saclay, CNRS * Tudor Groza, EMBL-EBI * Deepak Gupta, US National Library of Medicine * Thierry Hamon, LIMSI-CNRS, France * Sam Henry, Christopher Newport University, USA * William Hogan, UCSD, USA * Richard Jackson, AstraZeneca * Antonio Jimeno Yepes, IBM, Melbourne Area, Australia * Sarvnaz Karimi, CSIRO, Australia * Nazmul Kazi, University of North Florida, USA * Won Gyu KIM, US National Library of Medicine * Roman Klinger, University of Stuttgart, Germany * Anna Koroleva, Omdena * Andre Lamurias, NOVA School of Science and Technology, Lisbon, Portugal * Majid Latifi, Department of Computer Science, University of York, York, UK * Alberto Lavelli, FBK-ICT, Italy * Robert Leaman, US National Library of Medicine * Lung-Hao Lee, National Central University, Taiwan * Ulf Leser, Humboldt-Universität zu Berlin, Germany * Diwakar Mahajan, IBM Research, USA * Timothy Miller, Boston Childrens Hospital and Harvard Medical School, USA * Makoto Miwa, Toyota Technological Institute, Japan * Claire Nedellec, French national institute of agronomy (INRA) * Guenter Neumann, DFKI, Germany * Aurélie Névéol, LISN - CNRS, France * Mariana Neves, Hasso-Plattner-Institute at the University of Potsdam, Germany * Brian Ondov, Yale University, USA * Laura Plaza, Universidad Nacional de Educación a Distancia * Noon Pokaratsiri Goldstein, Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) * François Remy, Ghent University, Belgium * Francisco J. Ribadas-Pena, University of Vigo, Spain * Fabio Rinaldi, Dalle Molle Institute for Artificial Intelligence Research (IDSIA), Lugano * Kirk Roberts, UTHealth, Houston, Texas * Roland Roller, DFKI, Germany * Mourad Sarrouti, CLARA Analytics, USA * Peng Su, University of Delaware, USA * Madhumita Sushil, University of California, San Francisco, USA * Mario Sänger, Humboldt Universität zu Berlin, Germany * Andrew Taylor, Yale University School of Medicine, USA * Karin Verspoor, RMIT University, Australia * Davy Weissenbacher, Cedars-Sinai, Los Angeles, California, USA * Nathan M. White, James Cook University, Australia * W John Wilbur, US National Library of Medicine * Amelie Wührl, University of Stuttgart, Germany * Dongfang Xu, Cedars-Sinai, USA * Shweta Yadav, University of Illinois Chicago, USA * Jingqing Zhang, Imperial College London, UK * Ayah Zirikly, Johns Hopkins, USA * Pierre Zweigenbaum, LIMSI - CNRS, France