Deep Learning Mental Health Dialogue System
Mental health counseling remains a major challenge in modern society due to cost, stigma, fear, and unavailability. We posit that generative artificial intelligence (AI) models designed for mental health counseling could help improve outcomes by lowering barriers to access. To this end, we have deve...
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| Vydáno v: | International Conference on Big Data and Smart Computing s. 395 - 398 |
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| Jazyk: | angličtina |
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IEEE
01.02.2023
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| ISSN: | 2375-9356 |
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| Abstract | Mental health counseling remains a major challenge in modern society due to cost, stigma, fear, and unavailability. We posit that generative artificial intelligence (AI) models designed for mental health counseling could help improve outcomes by lowering barriers to access. To this end, we have developed a deep learning (DL) dialogue system called Serena. The system consists of a core generative model and post-processing algorithms. The core generative model is a 2.7 billion parameter Seq2Seq Transformer [26] fine-tuned on thousands of transcripts of person-centered-therapy (PCT) sessions. The series of postprocessing algorithms detects contradictions, improves coherency, and removes repetitive answers. Serena is implemented and deployed on https://serena.chat, which currently offers limited free services. While the dialogue system is capable of responding in a qualitatively empathetic and engaging manner, occasionally it displays hallucination and long-term incoherence. Overall, we demonstrate that a deep learning mental health dialogue system has the potential to provide a low-cost and effective complement to traditional human counselors with less barriers to access. |
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| AbstractList | Mental health counseling remains a major challenge in modern society due to cost, stigma, fear, and unavailability. We posit that generative artificial intelligence (AI) models designed for mental health counseling could help improve outcomes by lowering barriers to access. To this end, we have developed a deep learning (DL) dialogue system called Serena. The system consists of a core generative model and post-processing algorithms. The core generative model is a 2.7 billion parameter Seq2Seq Transformer [26] fine-tuned on thousands of transcripts of person-centered-therapy (PCT) sessions. The series of postprocessing algorithms detects contradictions, improves coherency, and removes repetitive answers. Serena is implemented and deployed on https://serena.chat, which currently offers limited free services. While the dialogue system is capable of responding in a qualitatively empathetic and engaging manner, occasionally it displays hallucination and long-term incoherence. Overall, we demonstrate that a deep learning mental health dialogue system has the potential to provide a low-cost and effective complement to traditional human counselors with less barriers to access. |
| Author | Dyer, George C. Gladka, Anna Chung, Neo Christopher Brocki, Lennart |
| Author_xml | – sequence: 1 givenname: Lennart surname: Brocki fullname: Brocki, Lennart email: brocki.lennart@gmail.com organization: Institute of Informatics University of Warsaw,Warsaw,Poland – sequence: 2 givenname: George C. surname: Dyer fullname: Dyer, George C. email: georgecdyer@gmail.com organization: Demiteris,Wrocław,Poland – sequence: 3 givenname: Anna surname: Gladka fullname: Gladka, Anna email: agladka@gmail.com organization: Wrocław Medical University,Psychiatry Department,Wrocław,Poland – sequence: 4 givenname: Neo Christopher surname: Chung fullname: Chung, Neo Christopher email: nchchung@gmail.com organization: Institute of Informatics University of Warsaw,Warsaw,Poland |
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| Snippet | Mental health counseling remains a major challenge in modern society due to cost, stigma, fear, and unavailability. We posit that generative artificial... |
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| SubjectTerms | Artificial Intelligence Big Data Chatbot Costs Deep learning Dialogue System Employee welfare Mental health Transformer cores Transformers |
| Title | Deep Learning Mental Health Dialogue System |
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