通过神经符号目标摘要与 Text-Units-Text 生成建模低资源健康辅导对话
Modeling Low-Resource Health Coaching Dialogues via Neuro-Symbolic Goal Summarization and Text-Units-Text Generation
AI总结：
针对低资源健康辅导成本高、依赖模式与标注的问题，提出神经符号目标摘要器和 Text-Units-Text 对话生成模型，并配套新数据集与回答非常规性指标，性能超过此前最优方法。
摘要：
健康辅导可帮助患者实现个性化且与生活方式相关的目标，从而有效管理慢性疾病并缓解心理健康问题。由于其高度个性化且劳动密集，健康辅导对低社会经济地位人群尤其有益，但成本高得令人难以承受。本文提出一种神经符号目标摘要器，以支持健康教练跟踪目标；并提出一个 Text-Units-Text 对话生成模型，用于与患者交谈，帮助他们制定并完成具体的体育活动目标。我们的模型优于此前的最先进方法，同时无需预定义模式及相应标注。本文还提出一个扩展了以往工作的新健康辅导数据集，以及一种基于数据难度衡量患者回答非常规性的指标，便于在部署过程中潜在地向教练发出警报。
英文摘要：
Health coaching helps patients achieve personalized and lifestyle-related goals, effectively managing chronic conditions and alleviating mental health issues. It is particularly beneficial, however cost-prohibitive, for low-socioeconomic status populations due to its highly personalized and labor-intensive nature. In this paper, we propose a neuro-symbolic goal summarizer to support health coaches in keeping track of the goals and a text-units-text dialogue generation model that converses with patients and helps them create and accomplish specific goals for physical activities. Our models outperform previous state-of-the-art while eliminating the need for predefined schema and corresponding annotation. We also propose a new health coaching dataset extending previous work and a metric to measure the unconventionality of the patient's response based on data difficulty, facilitating potential coach alerts during deployment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
