3 research outputs found
Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS
Suicide is the 10th leading cause of death in the U.S (1999-2019). However,
predicting when someone will attempt suicide has been nearly impossible. In the
modern world, many individuals suffering from mental illness seek emotional
support and advice on well-known and easily-accessible social media platforms
such as Reddit. While prior artificial intelligence research has demonstrated
the ability to extract valuable information from social media on suicidal
thoughts and behaviors, these efforts have not considered both severity and
temporality of risk. The insights made possible by access to such data have
enormous clinical potential - most dramatically envisioned as a trigger to
employ timely and targeted interventions (i.e., voluntary and involuntary
psychiatric hospitalization) to save lives. In this work, we address this
knowledge gap by developing deep learning algorithms to assess suicide risk in
terms of severity and temporality from Reddit data based on the Columbia
Suicide Severity Rating Scale (C-SSRS). In particular, we employ two deep
learning approaches: time-variant and time-invariant modeling, for user-level
suicide risk assessment, and evaluate their performance against a
clinician-adjudicated gold standard Reddit corpus annotated based on the
C-SSRS. Our results suggest that the time-variant approach outperforms the
time-invariant method in the assessment of suicide-related ideations and
supportive behaviors (AUC:0.78), while the time-invariant model performed
better in predicting suicide-related behaviors and suicide attempt (AUC:0.64).
The proposed approach can be integrated with clinical diagnostic interviews for
improving suicide risk assessments.Comment: 24 Pages, 8 Tables, 6 Figures; Accepted by PLoS One ; One of the two
mentioned Datasets in the manuscript has Closed Access. We will make it
public after PLoS One produces the manuscrip
HyperPrompt: Prompt-based Task-Conditioning of Transformers
Prompt-Tuning is a new paradigm for finetuning pre-trained language models in
a parameter-efficient way. Here, we explore the use of HyperNetworks to
generate hyper-prompts: we propose HyperPrompt, a novel architecture for
prompt-based task-conditioning of self-attention in Transformers. The
hyper-prompts are end-to-end learnable via generation by a HyperNetwork.
HyperPrompt allows the network to learn task-specific feature maps where the
hyper-prompts serve as task global memories for the queries to attend to, at
the same time enabling flexible information sharing among tasks. We show that
HyperPrompt is competitive against strong multi-task learning baselines with as
few as of additional task-conditioning parameters, achieving great
parameter and computational efficiency. Through extensive empirical
experiments, we demonstrate that HyperPrompt can achieve superior performances
over strong T5 multi-task learning baselines and parameter-efficient adapter
variants including Prompt-Tuning and HyperFormer++ on Natural Language
Understanding benchmarks of GLUE and SuperGLUE across many model sizes.Comment: Accepted to ICML 202