United States

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.

ACL 2026

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

cancer staging

openbiollm

bioclinical-modernbert

long-context encoders

biomedical large language models

pathological tnm staging

pathology reports

lora fine-tuning

clinical natural language processing

information extraction

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
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workshop paper

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.

Enigma at #SMM4H–HeaRD 2026: Leveraging Multilingual Pre-trained
Models for Clinical Named Entity Recognition

In this work, we address the detection of drug
abuse repercussions in Reddit posts, as part of
SMM4H-HeaRD Task 7: Extraction of Social
and Clinical Impacts of Substance Use from
Social Media Posts. We evaluate multiple ap-
proaches, including fine-tuning and zero-shot
inference, across several deep learning archi-
tectures. Our best result is obtained using an
adapter-based fine-tuning approach on the De-
BERTaV3 model. In addition, we explore text-
based evolutionary optimization for Gemma
4 workflows and show that, on this task, they
achieve competitive performance with the su-
pervised DeBERTaV3 setup.

RACAI at #SMM4H-HeaRD: Named Entity Recognition for Detecting the Impacts of Drug Abuse in Social Media Posts: Zero-Shot and Fine-Tuning Approaches

This paper presents our submission to the Mul-
tiClinAI Shared Task (Gallego-Donoso et al.,
2026) on cross-lingual clinical entity annota-
tion projection from Spanish to English. Our
system transfers expert annotations for Dis-
eases, Symptoms and Procedures entities. The
approach integrates three core components:
adaptive candidate window generation, an XG-
Boost classifier leveraging surface and seman-
tic features, and an LLM-based post-processing
stage to resolve complex misalignments. Our
highest-performing run ranked 3rd on the offi-
cial leaderboard, achieving strict F1 scores of
0.737, 0.549, and 0.538 for Diseases, Proce-
dures, and Symptoms, respectively. These re-
sults show that combining supervised candidate
scoring with targeted LLM refinement provides
a robust strategy for clinical entity projection.

ICB-UMA at #SMM4H–HeaRD 2026: Hybrid Clinical Entity Projection for MultiClinAI: Adaptive Candidate Windows, XGBoost, and LLM Refinement

This paper presents our submission to Multi-
ClinAI’s NER subtask for #SMM4H-HeaRD
2026. We focus on the questions 1) which
Language Model represents the clinical notes
best and 2) which annotations can help train-
ing these models. To get answers for these
questions, we follow a token-based classifica-
tion approach with pretrained encoder language
models, where we compare models that were
pretrained on generic data against medical data,
and on a single language, Dutch, against many
languages. In addition, we present two data-
augmented systems: one with data from the
other languages of the workshop for multilin-
gual training, and one with synthetic annota-
tions1
.

LotusOrchid at #SMM4H–HeaRD 2026: Fitting pretrained encoders for
Dutch medical data

This paper presents our approach to Task 5 of
the #SMM4H-HeaRD 2026 Workshop, which
focuses on detecting patient metadata in SARS-
CoV-2 sequencing articles as a binary classi-
fication task. We explore both encoder-based
and large language model (LLM) approaches,
using BioM-BERT as a baseline and Mistral-
Nemo as the LLM. To improve performance,
we propose a paraphrase-based data augmenta-
tion pipeline using Qwen3, where paraphrased
training and validation instances are added for
fine-tuning. For the LLM, we perform prompt
refinement and error analysis, while for the
encoder-based model, we reformulate the task
as a hypothesis-conditioned classification task
inspired by Natural Language Inference (NLI).
Our methods improve both models: Mistral-
Nemo increases from 0.423 to 0.750 F1, and
BioM-BERT from 0.801 to 0.821 on the valida-
tion set. Although Mistral-Nemo does not sur-
pass BioM-BERT, our best BioM-BERT model
achieves an F1-score of 0.786 on the test set,
outperforming the mean and median of com-
peting systems. To support reproducibility, we
release our best-performing model on Hugging
Face.

PEI at #SMM4H-HeaRD 2026: Enhancing Patient Metadata Detection via
Hypothesis-Conditioned Classification and Paraphrase-Based Data
Augmentation

We present a system for multilingual clini-
cal named entity recognition (NER) submitted
to the MultiClinNER subtask of MultiClinAI
2026, covering all seven languages and three
entity classes (disease, symptom, procedure).
Our approach trains one binary token classifier
ensemble per entity class using cross-lingual
fine-tuning of XLM-RoBERTa-large, with all
languages handled jointly. We apply character-
level ensembling over six models (two encoder
variants × three cross-validation folds). This
ensembling method provides more granular
probability estimates than single-model clas-
sifiers, allowing for more flexible precision-
recall trade-off tuning. The system achieves
character-level F1 scores of 0.70–0.88 on the
official test set.

Discovery@FI at #SMM4H–HeaRD 2026:
Ensemble Character Classifier for Multilingual Clinical NER

In this paper, we describe systems for two
#SMM4H-HeaRD 2026 shared tasks. Task 6
asks for per-axis TNM cancer staging from
free-text TCGA pathology reports under se-
vere label imbalance and long-document con-
straints. We fine-tune GatorTron-base sep-
arately on each axis using Focal loss with
class weights and a pooled [CLS]–mean rep-
resentation, reaching macro F1 of 0.700 (T),
0.774 (N), and 0.640 (M) on test set 2 against
a baseline of 0.454, 0.591, and 0.554 re-
spectively. Task 7 asks for span-level detec-
tion of opioid-related ClinicalImpacts and So-
cialImpacts in first-person Reddit posts. We
combine DeBERTa-large and PubMedBERT
(two seeds each) in a uniform-weight ensemble
with boundary-aware loss, entity-replacement
augmentation, and a first-person post filter,
achieving strict F1 of 0.51 and relaxed F1 of
0.60, above both the task mean (0.46 / 0.55) and
median (0.48 / 0.58).

CUET_DiagNLP at #SMM4H-HeaRD 2026: Per-Axis TNM Staging from
Pathology Reports and Opioid Impact Span Detection from Social Media

We present an overview of the MultiClinAI shared task, which focuses on multilingual clinical entity extraction and automatic corpus generation through annotation projection. It addresses two key challenges in clinical natural language processing (NLP): (i) developing comparable multilingual named entity recognition (NER) systems and (ii) automatically constructing multilingual clinical corpora through annotation projection. The MultiClinAI task provides a unified benchmark for evaluating multilingual and cross-lingual clinical NLP approaches that cover diseases, symptoms, and procedures in Spanish, English, Dutch, Italian, Romanian, Swedish, and Czech. A total of 21 teams from 13 countries participated, submitting 531 runs across the different subtasks. The top runs obtained very competitive results across several languages and entity types. The results highlight both the challenges and opportunities of multilingual clinical information extraction. All resources, including a corpus of over 738,201 manually revised entity mentions across seven languages, are publicly available on Zenodo at: https://zenodo.org/records/19334278.

The MultiClinAI Shared Task on Multilingual Clinical Corpus Construction and Concept Extraction: Systems, Evaluation, and Datasets

We present two systems for the #SMM4H-
HeaRD 2026 Task 2 (Lopez-Garcia et al., 2026)
shared task of automated insomnia detection
from clinical notes. Our system addresses both
subtasks: (1) binary insomnia classification and
(2) multi-label rule prediction with evidence
span extraction. For Subtask 1, we employ an
ensemble architecture combining Qwen3-4B-
Instruct1 and Bio_ClinicalBERT2 to capture
both general semantic reasoning and domain-
specific clinical representations. The frame-
work utilizes chunk-based processing with over-
lapping token windows to handle long clinical
notes efficiently. For Subtask 2, we develop
a dual-head multi-task transformer model that
jointly predicts insomnia labels and token-level
evidence spans using BIO tagging3. To im-
prove clinical relevance, we additionally incor-
porate sentence-level filtering using sentence-
transformer embeddings and similarity-based
retrieval of insomnia-related contexts. Experi-
mental results suggest competitive performance
relative to the shared task mean and median
scores across both subtasks. Our best Subtask 1
system achieves a recall of 0.9474, surpassing
the shared-task mean and median recall, while
our Subtask 2 system exceeds the mean and
median scores across label classification, exact
match, and partial match metrics. The end-
to-end implementation is publicly available on
GitHub.

NoviceTrio in #SMM4H-HeaRD 2026: Hybrid Clinical Transformer
Ensembles for Insomnia Detection and Evidence Extraction from Clinical
Notes

The Tumor, Node, and Metastasis (TNM) stag-
ing system is critical to cancer treatment. This
study aims to predict TNM stage labels in-
dependently, with the Cancer Genome Atlas
(TCGA) pathology report as the sixth shared
task of SMM4H-HeaRD 2026. The problem is
framed as three multi-label classification tasks.
We explore both classical and deep learning ap-
proaches using Term Frequency-Inverse Doc-
ument Frequency (TF-IDF) features and em-
beddings from ClinicalBERT, BioBERT, and
PubMedBERT. These representations are used
with Logistic Regression (LR), Light Gradient
Boosting Machine (LightGBM), Feed-Forward
Neural Networks (FFNN), and Wide Residual
Networks (WRN). Our results show that in-
dividual embeddings perform similarly to the
TNM label classification, while their combi-
nation improves its predictive ability. WRN
achieves AUROC scores of 0.839 (T), 0.8502
(N), and 0.803 (M) with F1-scores of 0.622,
0.702, and 0.9337, respectively, for the train-
ing phase. LightGBM with TF-IDF performs
best with AUROC scores of 0.9368 (T), 0.9524
(N), and 0.8311 (M) and F1-scores of 0.7559
(T), 0.7384 (N), and 0.7017 (M) during the
training phase. Furthermore, the result of the
Codabench for the test sets indicates a Macro-
F1 score of 0.978, 0.957, and 0.879 for the T,
N, and M categories respectively for test set
1; while test set 2 records a Macro-F1 score
for T, N, and M is 0.807, 0.767, 1.0 respec-
tively. However, performance declined during
the evaluation phase of the test sets, a drop from
0.938 for test set 1 to 0.858 for test set 2, for the
Macro-F1 score across all stages; suggesting
limitations in model generalizability, sensitivity
to class imbalance, and challenges in process-
ing lengthy clinical documents. Although this
study provides an efficient baseline model and
a reproducible pipeline, further optimization
and validation are required before it can be con-
sidered suitable for use in a real-world clinical
setting.

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