@inproceedings{kejriwal-etal-2026-infimobius,
title = "Infimobius at {\#}{SMM}4{H}-{H}ea{RD} 2026: Multi-Seed {D}e{BERT}a Ensemble for Flu Vaccination and Testing Status Classification",
author = "Kejriwal, Pradyumn and
Charan, Suhani Singh and
Sharma, Raksha and
Murthy, Rudra",
editor = "Lopez-Garcia, Guillermo and
Gonzalez-Hernandez, Graciela",
booktitle = "Proceedings of the 11th Social Media Mining for Health Research and Applications ({SMM}4{H}-{H}ea{RD} 2026) Workshop and Shared Tasks",
month = jul,
year = "2026",
address = "San Diego, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.smm4h-1.42/",
pages = "260--263",
ISBN = "979-8-89176-432-3",
abstract = "This paper describes FluENS (Flu ENsemble System), our submission to the Social Media Mining for Health (SMM4H) 2026 Shared Task 3, which targets fine-grained classification of flu vaccination and flu testing statuses from tweets. FluENS builds on the microsoft/deberta-v2-xlarge pre-trained language model and employs a multi-seed ensemble strategy in which five models, each initialized with a different random seed and trained on the full training set, are aggregated through soft-voting over averaged softmax probabilities. We additionally incorporate balanced class weights to mitigate severe label imbalance and apply a two-stage learning rate schedule that separately controls the encoder and classification head. On the development set, FluENS achieves a macro F1 of 79.64{\%} and micro F1 of 85.56{\%} on the flu vaccination sub-task, and a macro F1 of 96.35{\%} and micro F1 of 97.04{\%} on the flu testing sub-task, substantially outperforming a roberta-base baseline across all metrics."
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<abstract>This paper describes FluENS (Flu ENsemble System), our submission to the Social Media Mining for Health (SMM4H) 2026 Shared Task 3, which targets fine-grained classification of flu vaccination and flu testing statuses from tweets. FluENS builds on the microsoft/deberta-v2-xlarge pre-trained language model and employs a multi-seed ensemble strategy in which five models, each initialized with a different random seed and trained on the full training set, are aggregated through soft-voting over averaged softmax probabilities. We additionally incorporate balanced class weights to mitigate severe label imbalance and apply a two-stage learning rate schedule that separately controls the encoder and classification head. On the development set, FluENS achieves a macro F1 of 79.64% and micro F1 of 85.56% on the flu vaccination sub-task, and a macro F1 of 96.35% and micro F1 of 97.04% on the flu testing sub-task, substantially outperforming a roberta-base baseline across all metrics.</abstract>
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%0 Conference Proceedings
%T Infimobius at #SMM4H-HeaRD 2026: Multi-Seed DeBERTa Ensemble for Flu Vaccination and Testing Status Classification
%A Kejriwal, Pradyumn
%A Charan, Suhani Singh
%A Sharma, Raksha
%A Murthy, Rudra
%Y Lopez-Garcia, Guillermo
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, United States
%@ 979-8-89176-432-3
%F kejriwal-etal-2026-infimobius
%X This paper describes FluENS (Flu ENsemble System), our submission to the Social Media Mining for Health (SMM4H) 2026 Shared Task 3, which targets fine-grained classification of flu vaccination and flu testing statuses from tweets. FluENS builds on the microsoft/deberta-v2-xlarge pre-trained language model and employs a multi-seed ensemble strategy in which five models, each initialized with a different random seed and trained on the full training set, are aggregated through soft-voting over averaged softmax probabilities. We additionally incorporate balanced class weights to mitigate severe label imbalance and apply a two-stage learning rate schedule that separately controls the encoder and classification head. On the development set, FluENS achieves a macro F1 of 79.64% and micro F1 of 85.56% on the flu vaccination sub-task, and a macro F1 of 96.35% and micro F1 of 97.04% on the flu testing sub-task, substantially outperforming a roberta-base baseline across all metrics.
%U https://aclanthology.org/2026.smm4h-1.42/
%P 260-263
Markdown (Informal)
[Infimobius at #SMM4H-HeaRD 2026: Multi-Seed DeBERTa Ensemble for Flu Vaccination and Testing Status Classification](https://aclanthology.org/2026.smm4h-1.42/) (Kejriwal et al., SMM4H 2026)
ACL