SCH: Natural Language Processing for Enhanced Behavioral Counseling

随着越来越多的人寻求咨询帮助，自然语言处理(NLP)技术可以为越来越多的咨询专业人员提供支持，以提供专注于质量的服务。该项目的总体目标是向新一代自然语言处理系统迈进。预计它将对咨询的进行方式产生重大影响，因为它将提供新的方法来评估辅导员的有效性，并以自动编码的形式帮助他们进行持续的反馈和指导，以及话轮转换和语言建议。这将允许咨询专业人员和其他卫生保健从业者通过及时和具有成本效益的反馈来提高他们的咨询质量。该项目中开发的方法将为能够为从医生和护士到疾病管理教练和营养师等广泛的卫生保健提供者提供咨询互动提供支持的系统奠定基础。该项目将在行为咨询日益增长的领域的启发下，在NLP中寻求几个新的和独特的研究方向。具体而言，该项目以以下四个主要目标为目标。(1)创建一个包含大量注释的行为咨询大数据集，涉及多个行为，并涵盖多个线上和线下来源。(2)开发将神经网络的最新进展与编码咨询策略的符号表示相结合的方法，并使用这些方法对咨询师的行为进行分类，并根据先前与来访者的互动预测他们未来最有可能的行为。(3)开发自然语言生成模型，帮助辅导员进行对话，特别关注问题和反思的生成。这些方法将由生成性神经模型组成，这些模型从大型咨询数据集中学习，同时明确地建模咨询策略并整合专家知识库。(4)创建和评估整合NLP工具的框架，以便在培训中向辅导员提供反馈和指导。重要的是，该项目将纳入整个研究渠道中来自领域专家和最终用户的反馈，使用焦点小组来确定要包括在拟议研究对象的每个设计和实施阶段的需求、偏好和功能。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

As more people seek out counseling help, Natural Language Processing (NLP) technology can provide support for the growing number of counselor professionals to deliver quality-focused services. The overarching goal of this project is to make advances toward a new generation of NLP systems. It is expected to have significant implications in the way counseling is conducted because it will provide new ways to evaluate counselor effectiveness and assisting them with ongoing feedback and coaching in the form of automatic coding, as well as turn-taking and language suggestions. This will allow counselor professionals and other health care practitioners to improve the quality of their counseling through timely and cost-effective feedback. The methodology developed in this project will establish the foundations toward systems that can provide support for counseling interactions for a wide range of health care providers from physicians and nurses to disease management coaches and dietitians. The project will pursue several new and unique research directions in NLP inspired by the growing area of behavioral counseling. Specifically, the project targets the following four main objectives. (1) Create a large dataset of behavior counseling with extensive annotations, addressing several behaviors and covering several online and offline sources. (2) Develop methods that combine the recent advances in neural networks with symbolic representations encoding counseling strategies, and use these methods to classify counselor behaviors and predict their most likely future behavior based on previous interactions with the client. (3) Develop natural language generation models that will assist the counselors in their conversations, specifically focusing on the generation of questions and reflections. The methods will consist of generative neural models that learn from a large counseling dataset while explicitly modeling counseling strategies and integrating expert knowledge bases. (4) Create and evaluate a framework for the integration of NLP tools to provide feedback and coaching to counselors in training. Importantly, the project will incorporate feedback from domain experts and end users across the entire research pipeline, using focus groups to identify needs, preferences, and features to include in each design and implementation stage of the proposed research objectives.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.