mandatory README file<\/strong> containing a detailed description of the methodology used for each submitted run.<\/li>\n<\/ul>\n\n\n\nThe README must clearly explain the approach followed for each run (e.g., model architecture, training data, external resources, prompting strategy, hyperparameters, or projection method, if applicable).<\/p>\n\n\n\n
\n\n\n\nQ: Should prediction files include headers?<\/h3>\n\n\n\n
Yes. All .tsv<\/code> prediction files must include headers with exactly<\/strong> these columns: filename<\/code>, label<\/code>, start_span<\/code>, end_span<\/code>, text<\/code>.<\/strong><\/p>\n\n\n\n
\n\n\n\nQ: How do I submit my results?<\/h3>\n\n\n\n
Participants must submit their predictions as a ZIP file<\/strong> containing the required output files, following the submission format described on the task website. All official submissions must be uploaded to the CodaBench evaluation platform<\/strong>, which will be used for scoring and ranking.<\/p>\n\n\n\nIn addition, teams are strongly encouraged<\/strong> to send a copy of the submitted ZIP file by email to the task organizers as a backup record of the submission. Detailed submission instructions and example ZIP files will be provided before the evaluation phase opens.<\/p>\n\n\n\n
\n\n\n\nQ: Can I submit multiple runs or prediction files?<\/h3>\n\n\n\n
Yes. Participants may submit multiple runs during the evaluation phase, subject to the submission limits<\/strong> defined for each phase. Specifically, participants can submit a maximum of 5 runs<\/strong> or prediction files per entity type and language<\/strong>, noting that the task includes three entity types and seven languages.<\/p>\n\n\n\nAll submitted runs must comply with the required submission format<\/strong>.<\/p>\n\n\n\n
\n\n\n\nQ: Can I use additional training data or external resources?<\/h3>\n\n\n\n
Yes. Participants are allowed to use additional training data, external resources, and pre-trained models.
However, all such resources must be clearly described<\/strong> in the system description paper submitted by the team.<\/p>\n\n\n\n
\n\n\n\nQ: Are all entity types evaluated?<\/h3>\n\n\n\n
The datasets include annotations for diseases, symptoms, procedures, and medications<\/strong>.
However, only diseases, symptoms, and procedures are evaluated<\/strong>. Medication annotations are released for completeness but are not considered in the evaluation.<\/p>\n\n\n\n
\n\n\n\nQ: Are all languages evaluated jointly or independently?<\/h3>\n\n\n\n
Each language is evaluated independently<\/strong>.
Participants may submit results for any subset of the seven task languages, and submission for all languages is not mandatory.<\/p>\n\n\n\n
\n\n\n\nQ: Should prediction files include headers?<\/h3>\n\n\n\n
Yes. All prediction files must include headers<\/strong>, following the format specified in the submission guidelines.<\/p>\n\n\n\n
\n\n\n\nQ: When will the test data be released?<\/h3>\n\n\n\n
The test datasets will be released via Zenodo<\/strong>, according to the official task schedule.
Dataset links will be announced through the task website and the Google Group.<\/p>\n\n\n\n
\n\n\n\nQ: Is a paper submission required?<\/h3>\n\n\n\n
Yes. All registered teams are required to submit a system description paper<\/strong>.
To be included in the official workshop proceedings, at least one author must register for and present the work at the #SMM4H-HeaRD 2026 Workshop<\/strong>, which will be held online<\/strong>.<\/p>\n\n\n\n<\/p>\n","protected":false},"excerpt":{"rendered":"
FAQ For any questions related to the MultiClinAI shared task, please contact: Q: What is the goal of the MultiClinAI shared task? The goal of MultiClinAI is twofold:(i) to evaluate multilingual clinical entity recognition systems across seven languages, and(ii) to assess automatic methods for generating comparable multilingual clinical corpora through annotation projection. Participants are required […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-102","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/temu.bsc.es\/MultiClinAI\/wp-json\/wp\/v2\/pages\/102","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/temu.bsc.es\/MultiClinAI\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/temu.bsc.es\/MultiClinAI\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/temu.bsc.es\/MultiClinAI\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/temu.bsc.es\/MultiClinAI\/wp-json\/wp\/v2\/comments?post=102"}],"version-history":[{"count":6,"href":"https:\/\/temu.bsc.es\/MultiClinAI\/wp-json\/wp\/v2\/pages\/102\/revisions"}],"predecessor-version":[{"id":262,"href":"https:\/\/temu.bsc.es\/MultiClinAI\/wp-json\/wp\/v2\/pages\/102\/revisions\/262"}],"wp:attachment":[{"href":"https:\/\/temu.bsc.es\/MultiClinAI\/wp-json\/wp\/v2\/media?parent=102"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}