Summary of Chinese Mentalbert: Domain-adaptive Pre-training on Social Media For Chinese Mental Health Text Analysis, by Wei Zhai et al.
Chinese MentalBERT: Domain-Adaptive Pre-training on Social Media for Chinese Mental Health Text Analysis
by Wei Zhai, Hongzhi Qi, Qing Zhao, Jianqiang Li, Ziqi Wang, Han Wang, Bing Xiang Yang, Guanghui Fu
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the pressing issue of psychological crisis situations triggered by social media use. It aims to develop a pre-trained language model tailored for psychology, leveraging large-scale datasets from Chinese social media platforms and publicly available datasets. The proposed model, MentalBERT, integrates psychological lexicons into its pre-training masking mechanism, building upon an existing Chinese language model. Adaptive training enables the model’s specialization for the psychological domain. Evaluations on six public datasets demonstrate improvements compared to eight other models. Moreover, qualitative comparisons show that MentalBERT provides psychologically relevant predictions given masked sentences. This work contributes to the development of AI models capable of efficient analysis in psychology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to help computers understand and analyze psychological issues related to social media use. It creates a special language model for psychology, using big datasets from Chinese social media platforms and other publicly available sources. The new model, MentalBERT, is trained to recognize important psychological terms and concepts. This helps the model make more accurate predictions about psychological text analysis. The researchers tested their model on many public datasets and found that it performed better than others. They also showed that the model can provide insights into masked sentences that are relevant to psychology. |
Keywords
* Artificial intelligence * Language model
