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Found 9 papers, 4 papers with code
WWBP-SQT-lite: Multi-level Models and Difference Embeddings for Moments of Change Identification in Mental Health Forums
Psychological states unfold dynamically; to understand and measure mental health at scale we need to detect and measure these changes from sequences of online posts.
Understanding BERT’s Mood: The Role of Contextual-Embeddings as User-Representations for Depression Assessment
Many recent works in natural language processing have demonstrated ability to assess aspects of mental health from personal discourse.
SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Tasks
Social science NLP tasks, such as emotion or humor detection, are required to capture the semantics along with the implicit pragmatics from text, often with limited amounts of training data.
Human Language Modeling
Natural language is generated by people, yet traditional language modeling views words or documents as if generated independently.
Evaluating Contextual Embeddings and their Extraction Layers for Depression Assessment
Recent works have demonstrated ability to assess aspects of mental health from personal discourse.
MeLT: Message-Level Transformer with Masked Document Representations as Pre-Training for Stance Detection
Much of natural language processing is focused on leveraging large capacity language models, typically trained over single messages with a task of predicting one or more tokens.
Empirical Evaluation of Pre-trained Transformers for Human-Level NLP: The Role of Sample Size and Dimensionality
In human-level NLP tasks, such as predicting mental health, personality, or demographics, the number of observations is often smaller than the standard 768+ hidden state sizes of each layer within modern transformer-based language models, limiting the ability to effectively leverage transformers.
Autoregressive Affective Language Forecasting: A Self-Supervised Task
Human natural language is mentioned at a specific point in time while human emotions change over time.
Suicide Risk Assessment with Multi-level Dual-Context Language and BERT
Mental health predictive systems typically model language as if from a single context (e. g. Twitter posts, status updates, or forum posts) and often limited to a single level of analysis (e. g. either the message-level or user-level).
