Hanging in the Balance: Pivotal Moments in Crisis Counseling Conversations

• URL: http://arxiv.org/abs/2506.03941v1
• Date: Wed, 04 Jun 2025 13:31:58 GMT
• Title: Hanging in the Balance: Pivotal Moments in Crisis Counseling Conversations
• Authors: Vivian Nguyen, Lillian Lee, Cristian Danescu-Niculescu-Mizil,
• Abstract summary: We introduce an unsupervised computational method for detecting pivotal moments as they happen.<n>Our approach relies on the intuition that a moment is pivotal if our expectation of the outcome varies widely depending on what might be said next.<n>We then use our framework to explore the relation of the counselor's response during pivotal moments with the eventual outcome of the session.
• Score: 11.315381025450373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: During a conversation, there can come certain moments where its outcome hangs in the balance. In these pivotal moments, how one responds can put the conversation on substantially different trajectories leading to significantly different outcomes. Systems that can detect when such moments arise could assist conversationalists in domains with highly consequential outcomes, such as mental health crisis counseling. In this work, we introduce an unsupervised computational method for detecting such pivotal moments as they happen, in an online fashion. Our approach relies on the intuition that a moment is pivotal if our expectation of the outcome varies widely depending on what might be said next. By applying our method to crisis counseling conversations, we first validate it by showing that it aligns with human perception -- counselors take significantly longer to respond during moments detected by our method -- and with the eventual conversational trajectory -- which is more likely to change course at these times. We then use our framework to explore the relation of the counselor's response during pivotal moments with the eventual outcome of the session.

Related papers

• Time is On My Side: Dynamics of Talk-Time Sharing in Video-chat Conversations [8.063275432999513]
  An intrinsic aspect of every conversation is the way talk-time is shared between multiple speakers.<n>We introduce a computational framework for quantifying the conversation-level distribution of talk-time between speakers.<n>We apply this framework to a large dataset of video-chats between strangers.
  arXiv  Detail & Related papers  (2025-06-25T14:23:02Z)
• Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations [58.65755268815283]
  Many real dialogues are interactive, meaning an agent's utterances will influence their conversational partner, elicit information, or change their opinion. We use this fact to rewrite and augment existing suboptimal data, and train via offline reinforcement learning (RL) an agent that outperforms both prompting and learning from unaltered human demonstrations. Our results in a user study with real humans show that our approach greatly outperforms existing state-of-the-art dialogue agents.
  arXiv  Detail & Related papers  (2024-11-07T21:37:51Z)
• Taking a turn for the better: Conversation redirection throughout the course of mental-health therapy [9.654703213945467]
  We introduce a measure of the extent to which a certain utterance immediately redirects the flow of the conversation. We apply this new measure to characterize the development of patient-therapist relationships over multiple sessions in an online therapy platform. Our analysis reveals that patient control of the conversation's direction generally increases relative to that of the therapist as their relationship progresses.
  arXiv  Detail & Related papers  (2024-10-09T17:54:41Z)
• How Did We Get Here? Summarizing Conversation Dynamics [4.644319899528183]
  We introduce the task of summarizing the dynamics of conversations by constructing a dataset of human-written summaries. We evaluate whether such summaries can capture the trajectory of conversations via an established downstream task. We show that they help both humans and automated systems with this forecasting task.
  arXiv  Detail & Related papers  (2024-04-29T18:00:03Z)
• Emotional Listener Portrait: Realistic Listener Motion Simulation in Conversation [50.35367785674921]
  Listener head generation centers on generating non-verbal behaviors of a listener in reference to the information delivered by a speaker. A significant challenge when generating such responses is the non-deterministic nature of fine-grained facial expressions during a conversation. We propose the Emotional Listener Portrait (ELP), which treats each fine-grained facial motion as a composition of several discrete motion-codewords. Our ELP model can not only automatically generate natural and diverse responses toward a given speaker via sampling from the learned distribution but also generate controllable responses with a predetermined attitude.
  arXiv  Detail & Related papers  (2023-09-29T18:18:32Z)
• Multiscale Contextual Learning for Speech Emotion Recognition in Emergency Call Center Conversations [4.297070083645049]
  This paper presents a multi-scale conversational context learning approach for speech emotion recognition. We investigated this approach on both speech transcriptions and acoustic segments. According to our tests, the context derived from previous tokens has a more significant influence on accurate prediction than the following tokens.
  arXiv  Detail & Related papers  (2023-08-28T20:31:45Z)
• Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey [71.43956423427397]
  We aim to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
  arXiv  Detail & Related papers  (2022-07-20T13:37:57Z)
• Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy [83.61404191470126]
  We propose a new solution named I-Pro that can learn Proactive policy in the Interactive setting. Specifically, we learn the trade-off via a learned goal weight, which consists of four factors. The experimental results demonstrate I-Pro significantly outperforms baselines in terms of effectiveness and interpretability.
  arXiv  Detail & Related papers  (2022-04-07T14:11:31Z)
• Who Responded to Whom: The Joint Effects of Latent Topics and Discourse in Conversation Structure [53.77234444565652]
  We identify the responding relations in the conversation discourse, which link response utterances to their initiations. We propose a model to learn latent topics and discourse in word distributions, and predict pairwise initiation-response links. Experimental results on both English and Chinese conversations show that our model significantly outperforms the previous state of the arts.
  arXiv  Detail & Related papers  (2021-04-17T17:46:00Z)
• Balancing Objectives in Counseling Conversations: Advancing Forwards or Looking Backwards [18.460035325229683]
  We develop an unsupervised methodology to quantify how counselors manage balance. Our main intuition is that if an utterance can only receive a narrow range of appropriate replies, then its likely aim is to advance the conversation forwards. By applying this intuition, we can map each utterance to a continuous orientation axis that captures the degree to which it is intended to direct the flow of the conversation forwards or backwards.
  arXiv  Detail & Related papers  (2020-05-08T18:00:27Z)
• Detecting depression in dyadic conversations with multimodal narratives and visualizations [1.4824891788575418]
  In this paper, we develop a system that supports humans to analyze conversations. We demonstrate the ability of our system to take in a wide range of multimodal information and automatically generated a prediction score for the depression state of the individual.
  arXiv  Detail & Related papers  (2020-01-13T10:47:13Z)
• Attention over Parameters for Dialogue Systems [69.48852519856331]
  We learn a dialogue system that independently parameterizes different dialogue skills, and learns to select and combine each of them through Attention over Parameters (AoP) The experimental results show that this approach achieves competitive performance on a combined dataset of MultiWOZ, In-Car Assistant, and Persona-Chat.
  arXiv  Detail & Related papers  (2020-01-07T03:10:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.