International audience

Figure 1: Greta 2.0 system architecture: Green color indicates added modules to the Greta platform, blue indicates existing modules of the Greta platform following the SAIBA framework, gray indicates abstract concepts, and purple indicates types of XML files that exchange information between modules.Each new module in green is explained and described in the paper.

Motivational Interviewing (MI) is an approach to therapy that emphasizes collaboration and encourages behavioral change. To evaluate the quality of an MI conversation, client utterances can be classified using the MISC code as either Change Talk (CT), Sustain Talk (ST), or Follow/Neutral (F/N). The proportion of CT in an MI conversation positively correlates with therapy outcomes, making accurate classification of client utterances essential. This paper presents a classifier that accurately distinguishes between the three MISC classes (CT, ST, and F/N), leveraging multimodal features such as text, prosody, and facial expressivity. We annotate the publicly available AnnoMI dataset to train our model to collect multimodal information. Furthermore, we identify the modality that contributes most to the decision-making process, providing valuable insights into the interplay of different modalities during an MI conversation.

The demand for mental health services has outpaced available resources, resulting in long wait times for patients. A potential solution is to use virtual agents that perform motivational interviews. These agents can be rule-based, requiring expert knowledge, or data-driven, needing large datasets for training, which are often hard to obtain. Patient simulation can generate synthetic data as an alternative. Traditionally, this involved template utterances with a dialog manager or uncontrollable black box large language models LLMs. This study proposes a hybrid approach, leveraging both methods to see if LLMs can follow instructed dialog acts while generating natural, coherent utterances. Our study shows that the language model adheres to given conditions and that conditioning on dialog improves the naturalness and coherence of generated utterances, validating our approach for simulating patient responses.

The demand for mental health services has risen substantially in recent years, leading to challenges in meeting patient needs promptly.Virtual agents capable of emulating motivational interviews (MI) have emerged as a potential solution to address this issue, offering immediate support that is especially beneficial for therapy modalities requiring multiple sessions.However, developing effective patient simulation methods for training MI dialog systems poses challenges, particularly in generating syntactically and contextually correct, and diversified dialog acts while respecting existing patterns and trends in therapy data.This paper investigates data-driven approaches to simulate patients for training MI dialog systems.We propose a novel method that leverages time series models to generate diverse and contextually appropriate patient dialog acts, which are then transformed into utterances by a conditioned large language model.Additionally, we introduce evaluation measures tailored to assess the quality and coherence of simulated patient dialog.Our findings highlight the effectiveness of dialog act-conditioned approaches in improving patient simulation for MI, offering insights for developing virtual agents to support mental health therapy.

In this work, we present a socially interactive agent able to adapt its conversational strategies to maximize user's engagement during the interaction. For this purpose, we train our agent with simulated users using deep reinforcement learning. First, the agent estimates the simulated user's engagement depending on the latter's nonverbal behaviors and turn-taking status. This measured engagement is then used as a reward to balance the task of the agent (giving information) and its social goal (maintaining the user highly engaged). Agent's dialog acts may have different impact on the user's engagement depending on the latter's conversational preferences.