Neural Topic Modeling of Psychotherapy Sessions (2204.10189v2)

Abstract: In this work, we compare different neural topic modeling methods in learning the topical propensities of different psychiatric conditions from the psychotherapy session transcripts parsed from speech recordings. We also incorporate temporal modeling to put this additional interpretability to action by parsing out topic similarities as a time series in a turn-level resolution. We believe this topic modeling framework can offer interpretable insights for the therapist to optimally decide his or her strategy and improve psychotherapy effectiveness.

• The paper introduces a comparative analysis of neural topic models, identifying their effectiveness in extracting dialogue themes from therapy sessions.
• It benchmarks models such as NVDM, WTM, ETM, and BATM on a dataset of over 950 sessions to evaluate topic coherence and diversity.
• Temporal modeling reveals distinct session dynamics, offering actionable insights to refine therapeutic strategies and clinical decision-making.

An Expert Overview of "Neural Topic Modeling of Psychotherapy Sessions"

The paper "Neural Topic Modeling of Psychotherapy Sessions" explores the application of neural topic models to the domain of psychotherapy, aiming to derive interpretable insights from session transcripts linked to various psychiatric conditions. It presents a comparative analysis of different neural topic modeling techniques to evaluate their efficacy in identifying topical propensities in psychotherapy dialogues. Additionally, the paper introduces a temporal modeling aspect designed to help therapists by generating interpretable insights that can inform therapeutic strategies and potentially enhance psychotherapy session outcomes.

Methodological Approaches

The authors benchmark several state-of-the-art neural topic models, specifically:

1. Neural Variational Document Model (NVDM) and its variant NVDM-GSM;
2. Wasserstein Topic Model (WTM), including variants WTM-MMD and WTM-GMM;
3. Embedded Topic Model (ETM);
4. Bidirectional Adversarial Training Model (BATM).

These models are assessed on the Alex Street Counseling and Psychotherapy Transcripts dataset, which encompasses over 950 therapy sessions capturing dialogues between therapists and patients dealing with conditions such as anxiety, depression, schizophrenia, and suicidal intents. This dataset provides an extensive linguistic source covering over 200,000 conversational turns, offering a fertile ground for topic modeling.

Results and Evaluation

The evaluation of the topic models is conducted using a range of coherence and diversity metrics, which are crucial in understanding the cogency and breadth of the topics extracted by the models. The results underscore that the WTM and ETM exhibit high performance with respect to topic coherence and diversity. However, different coherence metrics yielded varying rankings, highlighting the complex landscape of evaluating topic models.

Moreover, the paper integrates temporal modeling to examine dialogue trajectories between patients and therapists. This approach revealed observable differences in session dynamics across psychiatric conditions—suicidal sessions spanned broader topic arcs, while schizophrenic sessions reflected a more diverse therapist engagement perhaps to distract from sensitive themes, indicating different therapeutic strategies in action.

Practical and Theoretical Implications

The findings suggest significant implications for the field of psychotherapy:

• Therapeutic Strategy Identification: By tracking topic trajectories, therapists can potentially identify effective strategic pivots during sessions, especially when dealing with sensitive or high-risk conditions.
• Intelligent Assistance: The framework sets a foundation for deploying AI-based assistants that could offer timely prompts and flag critical dialogue themes, enhancing clinical decision-making and therapy outcomes.
• Psychiatric Condition Profiling: Insights into how different psychiatric conditions influence dialogue patterns can enrich understanding of these disorders, informing targeted therapeutic approaches.

Future Directions

Beyond the current scope, this research can be extended to:

• Incorporating reinforcement learning frameworks to refine therapy chatbot interactions using derived topic insights.
• Further integrating biological and cognitive priors to deepen the interpretability of psychological states inferred from dialogue.
• Expanding the temporal analysis capabilities to support real-time feedback during psychotherapy sessions.

In conclusion, this work marks a pivotal step in leveraging neural topic modeling within psychotherapy, offering a multidimensional view into therapist-patient interactions and providing tools for data-driven enhancements to mental health treatments. While challenges remain, particularly in terms of model coherence and mapping across various mental health conditions, the research sets a robust precedent for future explorations into AI-facilitated psychotherapy.