Summary of Case: Efficient Curricular Data Pre-training For Building Assistive Psychology Expert Models, by Sarthak Harne et al.
CASE: Efficient Curricular Data Pre-training for Building Assistive Psychology Expert Models
by Sarthak Harne, Monjoy Narayan Choudhury, Madhav Rao, TK Srikanth, Seema Mehrotra, Apoorva Vashisht, Aarushi Basu, Manjit Sodhi
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study leverages Natural Language Processing (NLP) pipelines to analyze online mental health forum posts, identifying individuals requiring urgent professional attention. By pre-training these pipelines using curricular texts from institutes specializing in mental health, the authors mimic a psychologist’s training process. The study presents CASE-BERT, which flags potential mental health disorders based on forum text. CASE-BERT outperforms existing methods, achieving f1 scores of 0.91 for Depression and 0.88 for Anxiety. The code and data are publicly available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps psychologists quickly find people who need help with their mental health. They use special computer programs to look at online conversations about mental health. This helps them identify people who might be in crisis and need professional help right away. The problem is that there isn’t enough data or it’s private, so they used books from school for training the computers. The new program, CASE-BERT, does a great job of finding signs of depression and anxiety. |
Keywords
* Artificial intelligence * Attention * Bert * Natural language processing * Nlp
