United States

Word-level cross-lingual alignment is central to
annotation projection, translation auditing, and
cross-lingual faithfulness estimation, yet exist-
ing neural aligners are rarely adapted to special-
ized domains. In this paper, we introduce Clin-
icalAligner26AM, a large-context multilingual
aligner model for biomedical and clinical text
initialized from ClinicalEncoder26AM (Remy,
2026b). Our training recipe is inspired by
AWESoME Align (Dou and Neubig, 2021). We
build our soft alignment target by sharpening
with Sinkhorn–Knop optimal transport a cost
matrix established for parallel clinical texts and
conversations through the fusion of sentence-
level, phrase-level, and token-level signals. We
distill this sharpened alignment matrix directly
into our student aligner, by encouraging its
naive cosine-based token similarity scores to
match this target. At inference time, we project
source-span scores through the learned token
alignment matrix and decode the longest valid
high-scoring span in the target text, optionally
supported by MultiClinNER predictions sum-
marized in Appendix B. We evaluate CA26AM
on the MultiClinCorpus shared task, which
projects Spanish clinical entity annotations into
six target languages (Gallego-Donoso et al.,
2026; Lopez-Garcia et al., 2026). Our two sub-
mitted systems ranked respectively first and
second across all languages and entity types,
with character-weighted F1 scores above 0.95
in nearly all settings.

ACL 2026

Parallia at #SMM4H-HeaRD 2026:
ClinicalAligner26AM: A Cross-Lingual Aligner for Dataset Translation;
Evidences from the MultiClinCorpus Shared Task

ross-lingual

alignment

optimal transport

healthcare

clinical

multilingual

**Welcome to ACL-IJCNLP 2021!**

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

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We describe two system submissions to Task 4
of the SMM4H-HeaRD 2026 Shared Task
on automated SOAP note generation from
doctor–patient dialogues. Our first submis-
sion is a standalone entity-conditioned gen-
eration model: Mistral-7B-Instruct-v0.1 fine-
tuned with QLoRA on 8,529 MedSynth train-
ing dialogues, where both training and infer-
ence prompts include clinical entities extracted
and grouped by SOAP section. Our second
submission is a four-stage modular pipeline
that additionally incorporates a hybrid retrieval
stage and a rule-based verification stage. The
key finding of this work is that incorporating
structured clinical domain knowledge, in the
form of NER entities grouped by SOAP sec-
tion, directly into the generation prompt pro-
duces consistent and reliable improvements
over dialogue-only generation. Our four-stage
pipeline submission achieved an average score
of 0.54 on the official test set, ranking first on
the shared task leaderboard.

NU_DeepHealthNLP at #SMM4H-HeaRD 2026: Entity-Conditioned
Generation and a Four-Stage Pipeline for Automated SOAP Note
Generation

TNM cancer staging is a critical process for
characterizing tumor burden and guiding clini-
cal decisions. Nevertheless, its automated ex-
traction remains challenging due to the unstruc-
tured and heterogeneous nature of free-text
pathology reports. This paper describes the par-
ticipation of the URJC-Team in Task 6 of the
Social Media Mining for Health/Health Real-
World Data (#SMM4H-HeaRD) 2026 Shared
Tasks. It focuses on predicting TNM stag-
ing from pathology reports. The proposed
workflow combines hand-crafted regular ex-
pressions with a Large Language Model (LLM).
First, explicit TNM mentions are extracted us-
ing rule-based patterns. Then, any stage not
recovered by these rules is inferred by an LLM.
Overall, the proposal provides competitive re-
sults across all official shared-task phases.

URJC-Team at #SMM4H-HeaRD 2026: TNM Stage Extraction with a
Regex-LLM Workflow

Six tasks from the SMM4H–HeaRD 2026
workshop were addressed with task-specific
large-language-model (LLM) pipelines relying
on prompt engineering, strict structured (JSON)
responses, and deterministic rule sets. The
pipelines utilize no task-specific fine-tuning
and can be adapted across diverse clinical and
social media data. This study demonstrates that
general-purpose LLMs (gpt-5.4-mini and gpt-
5.4) can accurately extract and classify crucial
health information when constrained by strict
output schemas. Notably, our hybrid approach
achieved the best overall performance among
all participating systems for Task 2 (Insomnia
Detection).

MedMind AI at #SMM4H-HeaRD 2026: Data Extraction and Generation Using Prompt Engineering and Structured Outputs (Tasks 1–6)

Detecting insomnia from clinical narratives re-
quires both accurate classification and clini-
cally grounded reasoning with interpretable evi-
dence. We present our systems for the SMM4H-
HeaRD 2026 shared task, which leverages
MIMIC-III notes annotated with rule-based in-
somnia criteria and supporting evidence spans.
We explore two complementary approaches:
parameter-efficient fine-tuning of lightweight
models using QLoRA and LoRA, and few-shot
prompting of large language models for joint
reasoning and evidence extraction. Our best
system achieves an F1-score of 0.7333 on bi-
nary classification and a micro-F1 of 0.6535 on
multi-label rule prediction, with up to 0.5192
partial-match F1 for evidence extraction. Re-
sults show that lightweight fine-tuned models
can outperform larger models in classification,
while larger models demonstrate stronger rea-
soning but struggle with precise span localiza-
tion, highlighting a key gap in clinically inter-
pretable NLP systems.

A3S@C-DAC at #SMM4H-HeaRD 2026: Reasoning Meets Evidence:
LLMs for Interpretable Insomnia Detection with Evidence Extraction in
Clinical Notes

This paper describes the Gladiators system for
Task 1 of the SMM4H 2026 shared task on bi-
nary classification of adverse drug event (ADE)
mentions in multilingual social media posts.
Our system fine-tunes three XLM-RoBERTa-
large models with different random seeds us-
ing focal loss (α=0.75, γ=2.0) and 3×pos-
itive oversampling, then averages their pre-
dicted probabilities and applies per-language
threshold optimization. On the development
set, our ensemble achieves a pooled binary F1
of 0.7505. On the official test set—which in-
troduced surprise Farsi comprising 35.5% of
samples—our system achieves F1 = 0.6039,
above the competition mean (0.5465) and me-
dian (0.5798). We evaluated eleven approaches,
document key negative results including catas-
trophic translation augmentation failures, and
analyze precision–recall trade-offs under se-
vere class imbalance. Post-evaluation, a six-
model cross-regime ensemble improved dev F1
to 0.7585.

Gladiators at #SMM4H–HeaRD 2026: Multi-Seed XLM-RoBERTa
Ensemble with Focal Loss
and Per-Language Threshold Optimization for Multilingual Adverse Drug
Event Detection

This paper describes the participation of the
LSI_UNED team in the firt sub-task of Mul-
tiClinAI at the #SMM4H-HeaRD 2026 Work-
shop, which focuses on multilingual clinical
named entity recognition in seven languages.
The task requires identifying mentions of dis-
eases, procedures, and symptoms in clinical
case reports. We propose a set of systems
based on the W2NER architecture, with a sepa-
rate model trained for each language and entity
type. For Spanish, we use a RoBERTa-based
model with data augmentation from additional
NER resources, while English and Italian sys-
tems are based on different biomedical BERT
variants. Results show consistent performance
across languages, with the best overall results
obtained for Spanish. Data augmentation im-
proves recall and F1, while English and Italian
models achieve competitive but slightly lower
scores. Symptom recognition remains the most
challenging entity type across all languages.

LSI_UNED at #SMM4H–HeaRD 2026: Grid-Based Biomedical Named
Entity Recognition Across Languages and Entity Types

We describe the LLATMU systems submitted
to the #SMM4H–HeaRD 2026 shared tasks,
covering two related clinical text structuring
problems: dialogue-to-SOAP note generation
(Task 4) and TNM staging classification from
pathology reports (Task 6). Although the two
tasks differ in modeling paradigm—text gen-
eration versus supervised classification—both
require transforming unstructured clinical nar-
ratives into structured representations.
For Task 4, we instruction-tuned LLMs with
parameter-efficient adaptation and submitted
a QLoRA-based Ministral-3B system, achiev-
ing an official blind test average score of 0.53
and outperforming the task-wide mean and me-
dian. For Task 6, we formulate TNM predic-
tion as a three-head classification problem us-
ing BioClinical-ModernBERT-large with long-
context encoding, class-weighted loss, and
normalized partial-label training. The model
achieves a validation average macro-F1 of
0.9196 and continues to outperform the offi-
cial baseline on the more challenging tie-break
test set.
Across both tasks, our results suggest that ro-
bust data handling, stable fine-tuning, and task-
appropriate supervision are important for prac-
tical clinical NLP under constrained and imper-
fect shared-task settings.

LLATMU at #SMM4H–HeaRD 2026: Clinical Text Structuring with QLoRA-based Generation and Partial-Label TNM Classification

We present a lightweight, deterministic post-
processing approach for clinical text classifi-
cation based on entanglement between clini-
cally meaningful concepts. Our system was
developed for the SMM4H 2026 shared task on
insomnia detection and related information ex-
traction from clinical notes. For Subtask 1, we
introduce an entanglement-based rescue layer
that models dependencies between sleep dis-
turbance, daytime impairment, and evidence
of sleep-targeted medications. Applied as a
false-negative correction on top of an LLM
baseline, this approach improves recall while
preserving precision. In the official test set, the
rescue layer increases F1 by 25% without de-
grading precision (1.00). Local experiments
show larger gains on weaker runs, suggesting a
stabilizing effect on variable LLM output. For
Subtask 2, we implement an LLM-based sys-
tem for rule-based evidence and span extraction.
Results highlight the effectiveness of modeling
clinically grounded dependencies and suggest
directions to improve evidence extraction and
span matching.

In2Lab-TNT at #SMM4H-HeaRD 2026: An Application of QTT’sTerminological Entanglement to Leverage Insomnia Detection in ClinicalNotes

We describe our systems for #SMM4H-HeaRD
2026 Task 6, which requires predicting the T, N,
and M components of pathological TNM stage
from TCGA pathology reports. We explored
both discriminative long-context encoders and
generative biomedical LLMs. For the first
test set, our BioClinical-ModernBERT-large
ensemble achieved 0.993 micro-F1 and 0.915
macro-F1, improving over the BB-TEN base-
line scoring-log result of 0.947 micro-F1 and
0.780 macro-F1. For the harder second test
set, our OpenBioLLM-8B LoRA extractor im-
proved component macro-F1 over the organizer
baseline from 0.454 to 0.626 for T, from 0.591
to 0.758 for N, and from 0.554 to 1.000 for
M. These results suggest that long-context en-
coders are strong for explicit T and N evidence,
while constrained generative LLM extraction
can be effective for harder reports. The main
remaining weakness is rare-class T4 recogni-
tion.

GoBlueInformatics at #SMM4H-HeaRD 2026: Long-Context Encoders and
Generative Biomedical LLMs for Pathological TNM Stage Prediction*

This paper addresses the MultiClinAI chal-
lenge, subtask MultiClinNER, which focuses
on clinical Named Entity Recognition (NER)
across seven languages: Czech, Dutch, En-
glish, Italian, Romanian, Spanish, and Swedish.
The main goal of MultiClinNER is to identify
and extract clinical terms specifically related
to diseases, procedures, and symptoms from
discharge summaries. The paper explores a va-
riety of state-of-the-art methods, both monolin-
gual and multilingual, ranging from pretrained,
zero-shot, domain-adapted transformers to fine-
tuned transformer models, and demonstrates
the benefits of ensemble modeling. Data aug-
mentation through external resources signifi-
cantly enhanced the models’ ability to recog-
nize clinical entities. Both monolingual and
multilingual approaches showed complemen-
tary strengths depending on the language and
entity type. The average F1 score achieved
across the best models for each language and
category is 0.6502.

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