Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach

• URL: http://arxiv.org/abs/2101.07714v1
• Date: Tue, 19 Jan 2021 16:37:58 GMT
• Title: Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach
• Authors: Ashish Sharma, Inna W. Lin, Adam S. Miner, David C. Atkins, Tim Althoff
• Abstract summary: Psychologists have repeatedly demonstrated that empathy is a key component leading to positive outcomes in supportive conversations. Recent studies have shown that highly empathic conversations are rare in online mental health platforms. We introduce a new task of empathic rewriting which aims to transform low-empathy conversational posts to higher empathy.
• Score: 10.19931220479239
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online peer-to-peer support platforms enable conversations between millions of people who seek and provide mental health support. If successful, web-based mental health conversations could improve access to treatment and reduce the global disease burden. Psychologists have repeatedly demonstrated that empathy, the ability to understand and feel the emotions and experiences of others, is a key component leading to positive outcomes in supportive conversations. However, recent studies have shown that highly empathic conversations are rare in online mental health platforms. In this paper, we work towards improving empathy in online mental health support conversations. We introduce a new task of empathic rewriting which aims to transform low-empathy conversational posts to higher empathy. Learning such transformations is challenging and requires a deep understanding of empathy while maintaining conversation quality through text fluency and specificity to the conversational context. Here we propose PARTNER, a deep reinforcement learning agent that learns to make sentence-level edits to posts in order to increase the expressed level of empathy while maintaining conversation quality. Our RL agent leverages a policy network, based on a transformer language model adapted from GPT-2, which performs the dual task of generating candidate empathic sentences and adding those sentences at appropriate positions. During training, we reward transformations that increase empathy in posts while maintaining text fluency, context specificity and diversity. Through a combination of automatic and human evaluation, we demonstrate that PARTNER successfully generates more empathic, specific, and diverse responses and outperforms NLP methods from related tasks like style transfer and empathic dialogue generation. Our work has direct implications for facilitating empathic conversations on web-based platforms.

Related papers

• 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)
• APTNESS: Incorporating Appraisal Theory and Emotion Support Strategies for Empathetic Response Generation [71.26755736617478]
  Empathetic response generation is designed to comprehend the emotions of others. We develop a framework that combines retrieval augmentation and emotional support strategy integration. Our framework can enhance the empathy ability of LLMs from both cognitive and affective empathy perspectives.
  arXiv  Detail & Related papers  (2024-07-23T02:23:37Z)
• SoulChat: Improving LLMs' Empathy, Listening, and Comfort Abilities through Fine-tuning with Multi-turn Empathy Conversations [19.11368665202549]
  When large language models are applied in the field of psychological counseling, they often rush to provide universal advice. We constructed a multi-turn empathetic conversation dataset of more than 2 million samples. Experiments have shown that the empathy ability of LLMs can be significantly enhanced when finetuning by using multi-turn dialogue history.
  arXiv  Detail & Related papers  (2023-11-01T03:49:52Z)
• Facilitating Multi-turn Emotional Support Conversation with Positive Emotion Elicitation: A Reinforcement Learning Approach [58.88422314998018]
  Emotional support conversation (ESC) aims to provide emotional support (ES) to improve one's mental state. Existing works stay at fitting grounded responses and responding strategies which ignore the effect on ES and lack explicit goals to guide emotional positive transition. We introduce a new paradigm to formalize multi-turn ESC as a process of positive emotion elicitation.
  arXiv  Detail & Related papers  (2023-07-16T09:58:44Z)
• Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support [10.743204843534512]
  We develop Hailey, an AI-in-the-loop agent that provides just-in-time feedback to help participants who provide support (peer supporters) respond more empathically to those seeking help (support seekers) We show that our Human-AI collaboration approach leads to a 19.60% increase in conversational empathy between peers overall. We find a larger 38.88% increase in empathy within the subsample of peer supporters who self-identify as experiencing difficulty providing support.
  arXiv  Detail & Related papers  (2022-03-28T23:37:08Z)
• Coral: An Approach for Conversational Agents in Mental Health Applications [0.0]
  We present an approach for creating a generative empathetic open-domain robot that can be used for mental health applications. We leverage large scale pre-training and empathetic conversational data to make the responses more empathetic in nature. Our models achieve state-of-the-art results on the Empathetic Dialogues test set.
  arXiv  Detail & Related papers  (2021-11-16T15:15:58Z)
• Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes [50.569762345799354]
  We argue that two issues must be tackled at the same time: (i) identifying which word is the cause for the other's emotion from his or her utterance and (ii) reflecting those specific words in the response generation. Taking inspiration from social cognition, we leverage a generative estimator to infer emotion cause words from utterances with no word-level label.
  arXiv  Detail & Related papers  (2021-09-18T04:22:49Z)
• Exemplars-guided Empathetic Response Generation Controlled by the Elements of Human Communication [88.52901763928045]
  We propose an approach that relies on exemplars to cue the generative model on fine stylistic properties that signal empathy to the interlocutor. We empirically show that these approaches yield significant improvements in empathetic response quality in terms of both automated and human-evaluated metrics.
  arXiv  Detail & Related papers  (2021-06-22T14:02:33Z)
• A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support [11.736179504987712]
  We present a computational approach to understanding how empathy is expressed in online mental health platforms. We develop a novel unifying theoretically-grounded framework for characterizing the communication of empathy in text-based conversations.
  arXiv  Detail & Related papers  (2020-09-17T17:47:00Z)
• Towards Persona-Based Empathetic Conversational Models [58.65492299237112]
  Empathetic conversational models have been shown to improve user satisfaction and task outcomes in numerous domains. In Psychology, persona has been shown to be highly correlated to personality, which in turn influences empathy. We propose a new task towards persona-based empathetic conversations and present the first empirical study on the impact of persona on empathetic responding.
  arXiv  Detail & Related papers  (2020-04-26T08:51:01Z)

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.