Lifelong Machine Learning and Sequential Decision Making for Natural Language Interfaces

自然语言界面的终身机器学习和顺序决策

• 批准号: 312388-2013
• 负责人: Poupart, Pascal
• 金额: $ 3.21万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=662526
• 关键词: Lifelong; Machine; Learning; Sequential; Decision

What could be possible if we let a machine learn continuously over its lifetime? ****Objectives: The goal of this work is to develop lifelong learning algorithms with application to dialog management. At a theoretical level, this research will investigate and advance the principles by which a machine can gradually learn over a long period of time, discover new concepts and generalize concepts to new situations. This will be put in practice by developing open ended dialog systems for natural language interfaces. Personalization of such interfaces at the language level and adapting to the habits and preferences of the user will benefit tremendously from continuous learning.****Methods: Since continuous learning is a sequential process, the techniques developed will be in the framework of reinforcement learning. Particular emphasis will be put on the development of non-parametric techniques that do not make a closed world assumption by allowing new concepts (e.g., new words, expressions, habits, goals) to be discovered and represented on the fly. Hierarchical representations will be employed to organize and reason about the concepts from low level language units to high level user intentions. The techniques will be deployed in speech and text interfaces for smart phone applications.****Novelty and significance: Lifelong machine learning is a new research direction that remains vastly unexplored. This research will develop the theory and practice of this new paradigm, which will enable a new breed of intelligent systems. It will contribute to the next generation of natural user interfaces based on speech for smart phones, video-gaming and hands-free car consoles.**

如果我们让机器在其生命周期中不断学习，会有什么可能呢？* 目标：这项工作的目标是开发终身学习算法，并应用于对话管理。 在理论层面上，这项研究将研究和推进机器可以在很长一段时间内逐渐学习，发现新概念并将概念推广到新情况的原则。 这将通过为自然语言界面开发开放式对话系统来实现。 在语言层面上使这些界面个性化并适应用户的习惯和偏好，将从不断学习中受益匪浅。方法：由于连续学习是一个连续的过程，开发的技术将在强化学习的框架内。 将特别强调非参数技术的发展，这些技术通过允许新概念（例如，新的单词、表达、习惯、目标）被发现并在飞行中表现出来。 层次表示将被用来组织和推理从低层次语言单元到高层次用户意图的概念。 这些技术将部署在智能手机应用程序的语音和文本界面中。新奇和重要性：终身机器学习是一个新的研究方向，仍有待探索。 这项研究将发展这种新范式的理论和实践，这将使一个新的智能系统。 它将为下一代基于语音的自然用户界面做出贡献，适用于智能手机、视频游戏和免提汽车控制台。