Paper Submission Guidelines
Please follow the paper submission guidelines (long papers) of the ClinicalNLP workshop: https://clinical-nlp.github.io/2024/submission.html
Additional instructions related to the shared task papers:
Each participating team can submit one paper describing their approaches and systems for one or more subtasks. The paper should be titled:
TEAM at MEDIQA-CORR 2024: Sub-Title
TEAM at MEDIQA-M3G 2024: Sub-Title
Submissions must include clear scientific writing, a short overview of the related work, a full description of the developed models and their originality/specificity, description of additional data/models that were used, and a discussion of the results. It is important to describe the details well enough that the system could be replicated. You can report additional experiments and results in your papers, but please make it clear that they were obtained before/after the competition.
MEDIQA will follow a double-blind review process, papers must not include authors' names and affiliations. Reporting your team's name, rank, and results is allowed and will not be considered as disclosure of identity.
When using OpenReview to submit your papers, select the category: "shared task".
We would expect that most papers will be included in the proceedings.
Teams with accepted papers will be invited to present their work at the ClinicalNLP workshop either as presentations or posters. Paper selection for oral presentations will be based on the following criteria: approach novelty, research insights, and obtained results.
Code: Publishing your code is encouraged but not required for your paper acceptance. If you are planning to make your GitHub repo public before the publication of your paper, you can cite your shared task paper as follows: Title. Authors. Submitted to NAACL-ClinicalNLP 2024.
Citations: Please cite the following papers when referring to the task and dataset:
MEDIQA-CORR:
Overview paper for the task description and official results:
@inproceedings{mediqa-corr-task,
author = {Asma {Ben Abacha} and Wen-wai Yim and Yujuan Fu and Zhaoyi Sun and Fei Xia and Meliha Yetisgen},
title = {Overview of the MEDIQA-CORR 2024 Shared Task on Medical Error Detection and Correction},
booktitle = {Proceedings of the 6th Clinical Natural Language Processing Workshop},
month = {June},
year = {2024},
address = {Mexico City, Mexico},
publisher = {Association for Computational Linguistics},
year = {2024}}
Dataset paper:
@article{mediqa-corr-dataset,
author = {Asma {Ben Abacha} and Wen-wai Yim and Yujuan Fu and Zhaoyi Sun and Meliha Yetisgen and Fei Xia and Thomas Lin},
title = {MEDEC: A Benchmark for Medical Error Detection and Correction in Clinical Notes},
journal = {CoRR},
eprinttype = {arXiv},
year = {2024}}
MEDIQA-M3G:
Overview paper for the task description and official results:
@inproceedings{mediqa-m3g-task,
author = {Wen-wai Yim and Asma {Ben Abacha} and Yujuan Fu and Zhaoyi Sun and Fei Xia and Meliha Yetisgen and Martin Krallinger},
title = {Overview of the MEDIQA-M3G 2024 Shared Task on Multilingual and Multimodal Medical Answer Generation},
booktitle = {Proceedings of the 6th Clinical Natural Language Processing Workshop},
month = {June},
year = {2024},
address = {Mexico City, Mexico},
publisher = {Association for Computational Linguistics},
year = {2024}}
Dataset paper:
@article{mediqa-m3g-dataset,
author = {Wen-wai Yim and Yujuan Fu and Zhaoyi Sun and Asma {Ben Abacha} and Meliha Yetisgen and Fei Xia},
title = {DermaVQA: A Multilingual Visual Question Answering Dataset for Dermatology},
journal = {CoRR},
eprinttype = {arXiv},
year = {2024}}