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Logic-driven Indirect Supervision: An Application to Crisis Counseling.

Mattia Medina Grespan1, Meghan Broadbent2, Xinyao Zhang2

  • 1Kahlert School of Computing, University of Utah.

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|April 30, 2025
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Summary

This study introduces a novel logic-based method to improve automated analysis of crisis counseling conversations. By using inexpensive session-level data, it enhances the accuracy of utterance-level predictions, supporting counselors and improving patient care.

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Human-Computer Interaction

Background:

  • Text-based crisis counseling requires manual analysis for quality assurance, which is time-consuming and costly.
  • Existing methods for analyzing conversations often rely on expensive, post hoc utterance-level annotations.
  • Session-level annotations, though less granular, are more readily available and less expensive to obtain.

Purpose of the Study:

  • To investigate if inexpensive, potentially noisy session-level annotations can improve the accuracy of utterance-level predictions in crisis counseling conversations.
  • To propose and evaluate a novel logic-based indirect supervision approach for enhancing conversation analysis.
  • To leverage structural dependencies between session and utterance levels for improved modeling.

Main Methods:

  • Developed a logic-based indirect supervision framework to exploit relationships between session-level and utterance-level annotations.
  • Utilized declaratively stated structural dependencies to improve utterance modeling.
  • Conducted experiments comparing the proposed method against a strong multi-task baseline.

Main Results:

  • The proposed indirect supervision approach achieved a 3.5% f-score improvement over the baseline for utterance-level predictions.
  • Ablation studies confirmed that indirect supervision via logic rules enhances system consistency and robustness.
  • Demonstrated the effectiveness of using readily available, albeit noisy, session-level data for improving detailed conversation analysis.

Conclusions:

  • Inexpensive session-level annotations, when combined with logic-based indirect supervision, can significantly enhance the performance of automated text-based crisis counseling analysis.
  • The proposed method offers a more efficient and robust way to support counselors and improve the quality of care.
  • This approach provides a scalable solution for quality assurance and counselor support in digital mental health interventions.