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，以提供以质量为中心的服务。该项目的总体目标是朝着新一代NLP系统迈进。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辅导，以及话轮转换和语言建议。这将使咨询师专业人员和其他医疗保健从业人员通过及时和具有成本效益的反馈来提高他们的咨询质量。在这个项目中开发的方法将建立的基础系统，可以提供支持咨询互动的广泛的医疗保健提供者，从医生和护士疾病管理教练和营养师。该项目将追求几个新的和独特的研究方向在NLP的启发不断增长的行为咨询领域。具体而言，该项目针对以下四个主要目标。(1)创建一个具有广泛注释的行为咨询的大型数据集，解决几种行为并涵盖几个在线和离线来源。(2)开发出联合收割机，将神经网络的最新进展与编码咨询策略的符号表征相结合，并使用这些方法对咨询师的行为进行分类，并根据与客户的先前互动预测他们最可能的未来行为。(3)开发自然语言生成模型，帮助辅导员进行对话，特别是关注问题和思考的生成。这些方法将由生成神经模型组成，这些模型从大型咨询数据集中学习，同时明确建模咨询策略并整合专家知识库。 (4)创建和评估NLP工具整合的框架，为培训中的辅导员提供反馈和指导。重要的是，该项目将在整个研究管道中纳入来自领域专家和最终用户的反馈，使用焦点小组来确定需求，偏好和功能，包括在拟议研究目标的每个设计和实施阶段。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.