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    <abstract>We present Team Paradise’s systems for three tasks in the SMM4H-HeaRD 2026 shared task: multilingual adverse drug event detection (Task 1), influenza vaccine effectiveness estimation via two-subtask classification (Task 3), and opioid impact span extraction (Task 7). For Task 1, threshold-only ablation on XLMRoBERTa-large achieves a macro-F1 of 0.597, exceeding the field mean (0.547) by +0.050. For Task 3, a three-stage hybrid pipeline combining twitter-RoBERTa-base-2022 with rule-based post-processing achieves Micro-F1 0.8434 (Subtask 1: vaccination status) and 0.8936 (Subtask 2: test results). For Task 7, RoBERTa-large with CRF decoding and sliding-window inference obtains relaxed F1 0.60 despite severe train-test distributional shift Across tasks, we identify class imbalance, temporal ambiguity, and platform heterogeneity as central challenges.</abstract>
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%0 Conference Proceedings
%T Team Paradise at #SMM4H-HeaRD 2026: Multi-Task Approaches for Social Media Health Mining
%A Goyal, Dhruv
%A Gupta, Ishita
%A Bedi, Jatin
%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 goyal-etal-2026-team
%X We present Team Paradise’s systems for three tasks in the SMM4H-HeaRD 2026 shared task: multilingual adverse drug event detection (Task 1), influenza vaccine effectiveness estimation via two-subtask classification (Task 3), and opioid impact span extraction (Task 7). For Task 1, threshold-only ablation on XLMRoBERTa-large achieves a macro-F1 of 0.597, exceeding the field mean (0.547) by +0.050. For Task 3, a three-stage hybrid pipeline combining twitter-RoBERTa-base-2022 with rule-based post-processing achieves Micro-F1 0.8434 (Subtask 1: vaccination status) and 0.8936 (Subtask 2: test results). For Task 7, RoBERTa-large with CRF decoding and sliding-window inference obtains relaxed F1 0.60 despite severe train-test distributional shift Across tasks, we identify class imbalance, temporal ambiguity, and platform heterogeneity as central challenges.
%U https://aclanthology.org/2026.smm4h-1.48/
%P 305-308

Markdown (Informal)

[Team Paradise at #SMM4H-HeaRD 2026: Multi-Task Approaches for Social Media Health Mining](https://aclanthology.org/2026.smm4h-1.48/) (Goyal et al., SMM4H 2026)

• Team Paradise at #SMM4H-HeaRD 2026: Multi-Task Approaches for Social Media Health Mining (Goyal et al., SMM4H 2026)

ACL

• Dhruv Goyal, Ishita Gupta, and Jatin Bedi. 2026. Team Paradise at #SMM4H-HeaRD 2026: Multi-Task Approaches for Social Media Health Mining. In Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks, pages 305–308, San Diego, United States. Association for Computational Linguistics.