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：本工作的目标是开发应用于对话管理的终身学习算法。在理论层面上，这项研究将调查和推进机器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理。这将通过为自然语言界面开发开放式对话系统来实现。在语言层面对这些界面进行个性化，并适应用户的习惯和偏好，将从持续学习中受益匪浅。****方法：由于持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，这种技术不会通过允许动态发现和表示新概念（例如，新单词、新表达、新习惯、新目标）来做出封闭世界的假设。层次表示将被用来组织和推理从低级语言单位到高级用户意图的概念。该技术将应用于智能手机应用程序的语音和文本界面。****新颖性和意义：终身机器学习是一个尚未被探索的新研究方向。这项研究将发展这种新范式的理论和实践，这将使新型智能系统成为可能。它将为智能手机、视频游戏和免提汽车控制台提供基于语音的下一代自然用户界面