GRACE (Granular Recognition of Argumentative Clinical Evidence)         

GRACE @ https://sites.google.com/view/iberlef-20... target="_blank" rel="noopener">IberLEF 2026 announces the first edition of a novel task on Argument Mining shared task in Spanish connecting Explainable AI and Evidence-Based Medicine across clinical trials and medical licensing examinations.

The Black Box Problem

Deep Learning models in healthcare deliver high performance but remain opaque barriers to clinical adoption. Explainable AI is now an essential requirement.

Argument Mining

AM automatically extracts claims and evidence from clinical text and reveals how they support or challenge each other, enabling transparent, traceable clinical reasoning.

Spanish, First

GRACE is the first Argument Mining task in Spanish for the clinical domain, filling a key gap in multilingual biomedical NLP with fine-grained, entity-level annotations.

Track 01

Clinical Trial Evidence & Argumentation

This track focuses on abstracts of Randomized Controlled Trials (RCTs). Their standardized design, contrasting an intervention with a control group, provides a transparent path from data to conclusions, making argumentative components more accessible to automated systems.

Goal: Identify argumentative components (claims and premises) and detect support/attack relations at the sentence level.

Track 02

Clinical Case Reasoning (MIR)

This track uses cases from the MIR (Médico Interno Residente) exam, Spain's national medical specialization test. Each instance pairs a dense clinical narrative with five competing diagnostic or treatment options, only one of which is correct.

Goal: Extract fine-grained evidence spans that justify the correct option while refuting the incorrect alternatives.

📅 Important Dates

Release of Training & Dev Sets: March 18

Official Test Set Release: April 22

Deadline for Result Submission: May 3

Publication of Results: May 8

System Paper Submission: May 24

Notification of Acceptance: June 17

IberLEF Workshop (at SEPLN): September 22

https://www.codabench.org/competitions/1... target="_blank" rel="noopener">More information

Iker de la Iglesia, HiTZ Center - Universidad del País Vasco (UPV/EHU), Spain

Aitziber Atutxa, HiTZ Center - Universidad del País Vasco (UPV/EHU), Spain

Ander Barrena, HiTZ Center - Universidad del País Vasco (UPV/EHU), Spain

Koldo Gojenola, HiTZ Center - Universidad del País Vasco (UPV/EHU), Spain

Raquel Martínez, NLP&IR - Universidad Nacional de Educación a Distancia (UNED), Spain

Soto Montalvo, Universidad Rey Juan Carlos (URJC), Spain

Miguel Ángel Rodriguez, NLP&IR - Universidad Nacional de Educación a Distancia (UNED), Spain

Sofia Zakhir Puig, IULMA - Universitat de València (UV), Spain

Vanesa Gómez Martinez, Universidad Rey Juan Carlos (URJC), Spain