한국정보처리학회:학술대회논문집 (Annual Conference of KIPS)

한국정보처리학회 (Korea Information Processing Society)
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Optimizing Empathy Prediction in Text-Based Mental Health Support with Multi-Task Learning and Imbalance Mitigation

  • Anjitha Divakaran (Dept. of AI Convergence, Chonnam National University) ;
  • Hyung-Jeong Yang (Dept. of AI Convergence, Chonnam National University) ;
  • Seung-won Kim (Dept. of AI Convergence, Chonnam National University) ;
  • Ji-eun Shin (Dept. of Psychology, Chonnam National University) ;
  • Soo-Hyung Kim (Dept. of AI Convergence, Chonnam National University)
  • 발행 : 2024.10.31
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초록

Empathy plays a crucial role in effective mental health support, particularly on text-based platforms where individuals seek understanding and compassion. Building on previous work that predicted empathy in separate models, we propose a multi-task learning approach that jointly models three empathy communication mechanisms: emotional reactions, interpretations, and explorations. By integrating RoBERTa with GRU layers, our model improves the accuracy and efficiency of empathy detection. Additionally, we address class imbalance by incorporating focal loss, which helps the model focus on underrepresented strong empathy signals. Our experiments show that the multi-task model, combined with focal loss, significantly improves performance across all empathy dimensions, making it a promising tool for enhancing real-time empathy feedback in online mental health support systems.

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과제정보

This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the Innovative Human Resource Development for Local Intellectualization support program (IITP-2023-RS-2022-00156287) supervised by the Institute for Information & communications Technology Planning & Evaluation (IITP). Additionally, it was funded by IITP under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2023-RS-2023-00256629) grant, and by the National Research Foundation of Korea (NRF) through the Korea government (MSIT) grant (RS2023-00219107).

참고문헌

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