III: Small: Exploiting the Massive User Generated Utterances for Intent Mining under Scarce Annotations

随着人工智能和机器学习技术的进步，用户通过口语与计算设备进行交互，以搜索信息或完成任务，这一点在智能家居、汽车、教育、医疗保健、零售和电信环境中基于语音的个人辅助产品中得到了体现。该项目研究用户意图挖掘，旨在从用户生成的话语中理解潜在的目标或目的。例如，用户询问个人辅助系统“我明天要带伞吗？”，就表明了获取天气信息的意图。由于问题中存在多种隐式表达，意图挖掘一直是信息搜索难以实现的目标，而在会话系统中完成任务则更加困难。例如，通过语音命令“预定我附近的餐厅”，系统将学习跟进日期或饮食偏好问题，并根据用户的反应细化任务目标，即意图。该项目探索了新的计算技术来理解用户生成的话语，同时解决了用于意图挖掘的注释数据的稀缺性。研究结果和见解有望导致更好的自然语言理解和对话管理，减少对人类注释工作的需求。本研究将适用于设计新的问题/保护理解系统，在降低标注成本的同时提高服务和用户满意度。研究项目将吸引研究生和本科生参与。研究成果将纳入课程设置。提出的项目通过制定四个基本意图挖掘任务，涵盖挖掘用户意图的发现、注释、无监督学习和顺序建模阶段，为从用户生成的话语中挖掘意图的基础提供了重大进展。研究任务的重点是处理标注稀缺性问题，因为从各种各样的噪声话语中获得准确定义和正确标注用户意图的大规模标注数据是费时费力的。该项目将包括意图发现、联合意图和槽注释、无监督意图学习和意图演化建模的原理、模型和算法的开发。丰富的学习模式，如零学习、强化学习、生成建模和多模态学习，将被引入到不断密集的场景中，在这些场景中，当前的学习原理没有足够的注释数据来获得开箱即用的成功。研究小组计划与研究界分享结果，包括数据集和软件，以促进未来的研究。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

With the advance of artificial intelligence and machine learning technology, users interact with computational devices through spoken language to search information or accomplish tasks, as is evident by voice-based personal assistance products in smart home, automobile, education, healthcare, retail, and telecommunications environments. This project studies user intent mining that aims to understand the underlying goals or purposes from user-generated utterances. For example, by asking the personal assistance system "should I bring an umbrella tomorrow?", a user reveals the intention of getting weather information. Intent mining has been an elusive goal for information search due to diverse, implicit expressions in questions, and it is even harder for task accomplishment in conversational systems. For example, by giving a voice command "book a restaurant near me", the system shall learn to follow up with date or dietary preferences questions and refine the task goal, i.e., the intent, according to the user response. This project explores new computational techniques to understand user-generated utterances while addressing the scarcity of annotation data available for intent mining. The research findings and insights are expected to lead to better natural language understanding, dialogue management with reduced requirements on human annotation efforts. The proposed research will be applicable to the design of new question/conservation understanding systems that improve service, user satisfaction with reduced annotation cost. The research projects will engage graduate and undergraduate students to participate in. Research findings will be incorporated into course curriculum. The proposed project provides major advancements to the foundation of intent mining from user-generated utterances, by formulating four fundamental intent mining tasks that cover the discovery, annotation, unsupervised learning and sequential modeling phase in mining user intentions. The research tasks are proposed with a specific and consistent focus on dealing with the labeling scarcity issue as it is time-consuming and labor-intensive to obtain a large scale labeled data where user intents are accurately defined and correctly annotated from diverse and noise utterances. The project will include developments of principles, models and algorithms for intent discovery, joint intent and slot annotation, unsupervised intent learning and intent evolvement modeling. Abundant learning schemas such as zero-shot learning, reinforcement learning, generative modeling, and multi-modal learning will be introduced for the ever-intensive scenario where there is not enough annotation data for current learning rationales to succeed out-of-the-box. The research team plans to share results, including datasets and software, with the research community to facilitate future studies.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.