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Response-act Guided Reinforced Dialogue Generation for Mental Health Counseling

2023-01-30 08:53:35
Aseem Srivastava, Ishan Pandey, Md. Shad Akhtar, Tanmoy Chakraborty

Abstract

Virtual Mental Health Assistants (VMHAs) have become a prevalent method for receiving mental health counseling in the digital healthcare space. An assistive counseling conversation commences with natural open-ended topics to familiarize the client with the environment and later converges into more fine-grained domain-specific topics. Unlike other conversational systems, which are categorized as open-domain or task-oriented systems, VMHAs possess a hybrid conversational flow. These counseling bots need to comprehend various aspects of the conversation, such as dialogue-acts, intents, etc., to engage the client in an effective conversation. Although the surge in digital health research highlights applications of many general-purpose response generation systems, they are barely suitable in the mental health domain -- the prime reason is the lack of understanding in mental health counseling. Moreover, in general, dialogue-act guided response generators are either limited to a template-based paradigm or lack appropriate semantics. To this end, we propose READER -- a REsponse-Act guided reinforced Dialogue genERation model for the mental health counseling conversations. READER is built on transformer to jointly predict a potential dialogue-act d(t+1) for the next utterance (aka response-act) and to generate an appropriate response u(t+1). Through the transformer-reinforcement-learning (TRL) with Proximal Policy Optimization (PPO), we guide the response generator to abide by d(t+1) and ensure the semantic richness of the responses via BERTScore in our reward computation. We evaluate READER on HOPE, a benchmark counseling conversation dataset and observe that it outperforms several baselines across several evaluation metrics -- METEOR, ROUGE, and BERTScore. We also furnish extensive qualitative and quantitative analyses on results, including error analysis, human evaluation, etc.

Abstract (translated)

虚拟精神健康助手(VMHAs)已经成为数字医疗保健空间中普遍接受的心理卫生咨询服务方法。辅助性对话开始于自然、开放性的话题,以便使客户熟悉环境,并随后逐渐聚焦于更精细的特定领域主题。与被视为开放领域或任务导向系统的其他对话系统不同,VMHAs具有混合对话流。这些咨询机器人需要理解对话的各种方面,例如对话行动、意图等,以与客户进行有效的对话。尽管数字健康研究突出了许多通用响应生成系统的应用范围,但它们在心理健康领域几乎不适用--主要原因是心理健康咨询的理解不足。此外,一般来说,对话行动指导响应生成模型要么局限于模板范式,要么缺乏适当的语义。为此,我们提出了Reader-Act guided Dialogue Generation Model,以用于心理卫生咨询服务对话。Reader是基于Transformer构建的,通过联合预测下一句可能的对话行动(即响应行动)和生成适当的响应u(t+1)。通过Transformer-Reinforcement-Learning(TRL)与PPO,我们指导响应生成模型遵守d(t+1)并通过BERTScore在奖励计算中确保响应的语义丰富性。我们评估了H Hope,这是一个基准对话对话数据集,并观察到它在多个评估指标上优于多个基准模型-- METEOR、ROUGE和BERTScore。我们还提供了广泛的定性和定量分析,包括错误分析、人类评估等。

URL

https://arxiv.org/abs/2301.12729

PDF

https://arxiv.org/pdf/2301.12729.pdf


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