@inproceedings{liyanage-etal-2023-augmenting,
title = "Augmenting {R}eddit Posts to Determine Wellness Dimensions impacting Mental Health",
author = "Liyanage, Chandreen and
Garg, Muskan and
Mago, Vijay and
Sohn, Sunghwan",
editor = "Demner-fushman, Dina and
Ananiadou, Sophia and
Cohen, Kevin",
booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.bionlp-1.27",
doi = "10.18653/v1/2023.bionlp-1.27",
pages = "306--312",
abstract = "Amid ongoing health crisis, there is a growing necessity to discern possible signs of Wellness Dimensions (WD) manifested in self-narrated text. As the distribution of WD on social media data is intrinsically imbalanced, we experiment the generative AI techniques for data augmentation to enable further improvement in the pre-screening task of classifying WD. To this end, we propose a simple yet effective data augmentation approach through prompt-based Generative AI models, and evaluate the ROUGE scores and syntactic/ semantic similarity among existing interpretations and augmented data. Our approach with ChatGPT model surpasses all the other methods and achieves improvement over baselines such as Easy-Data Augmentation (EDA) and Backtranslation (BT).",
}
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<abstract>Amid ongoing health crisis, there is a growing necessity to discern possible signs of Wellness Dimensions (WD) manifested in self-narrated text. As the distribution of WD on social media data is intrinsically imbalanced, we experiment the generative AI techniques for data augmentation to enable further improvement in the pre-screening task of classifying WD. To this end, we propose a simple yet effective data augmentation approach through prompt-based Generative AI models, and evaluate the ROUGE scores and syntactic/ semantic similarity among existing interpretations and augmented data. Our approach with ChatGPT model surpasses all the other methods and achieves improvement over baselines such as Easy-Data Augmentation (EDA) and Backtranslation (BT).</abstract>
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%0 Conference Proceedings
%T Augmenting Reddit Posts to Determine Wellness Dimensions impacting Mental Health
%A Liyanage, Chandreen
%A Garg, Muskan
%A Mago, Vijay
%A Sohn, Sunghwan
%Y Demner-fushman, Dina
%Y Ananiadou, Sophia
%Y Cohen, Kevin
%S The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F liyanage-etal-2023-augmenting
%X Amid ongoing health crisis, there is a growing necessity to discern possible signs of Wellness Dimensions (WD) manifested in self-narrated text. As the distribution of WD on social media data is intrinsically imbalanced, we experiment the generative AI techniques for data augmentation to enable further improvement in the pre-screening task of classifying WD. To this end, we propose a simple yet effective data augmentation approach through prompt-based Generative AI models, and evaluate the ROUGE scores and syntactic/ semantic similarity among existing interpretations and augmented data. Our approach with ChatGPT model surpasses all the other methods and achieves improvement over baselines such as Easy-Data Augmentation (EDA) and Backtranslation (BT).
%R 10.18653/v1/2023.bionlp-1.27
%U https://aclanthology.org/2023.bionlp-1.27
%U https://doi.org/10.18653/v1/2023.bionlp-1.27
%P 306-312
Markdown (Informal)
[Augmenting Reddit Posts to Determine Wellness Dimensions impacting Mental Health](https://aclanthology.org/2023.bionlp-1.27) (Liyanage et al., BioNLP 2023)
ACL