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Found 6 papers, 2 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.
Comparing Pre-trained Human Language Models: Is it Better with Human Context as Groups, Individual Traits, or Both?
We compare pre-training models with human context via 1) group attributes, 2) individual users, and 3) a combined approach on 5 user- and document-level tasks.
Large Human Language Models: A Need and the Challenges
As research in human-centered NLP advances, there is a growing recognition of the importance of incorporating human and social factors into NLP models.
Robust language-based mental health assessments in time and space through social media
Compared to physical health, population mental health measurement in the U. S. is very coarse-grained.
Human Language Modeling
Natural language is generated by people, yet traditional language modeling views words or documents as if generated independently.
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.
