论文标题
强化学习和语言处理的匪徒：教程，审查和看法
Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook
论文作者
论文摘要
近年来，强化学习和匪徒改变了许多现实世界的应用程序，包括医疗保健，金融，推荐系统，机器人技术以及最后但并非最不重要的一点，即语音和自然语言处理。尽管强化学习算法的大多数语音和语言应用都集中在改善其灵活优化属性的深度神经网络的培训，但仍有许多理由需要探索以利用强化学习的益处，例如其奖励驱动的适应性，状态，状态表示，时间结构和概括性。在这项调查中，我们概述了强化学习和土匪的最新进步，并讨论如何通过适应性，互动和可扩展的模型有效地使用它们来解决语音和自然语言处理问题。
In recent years, reinforcement learning and bandits have transformed a wide range of real-world applications including healthcare, finance, recommendation systems, robotics, and last but not least, the speech and natural language processing. While most speech and language applications of reinforcement learning algorithms are centered around improving the training of deep neural networks with its flexible optimization properties, there are still many grounds to explore to utilize the benefits of reinforcement learning, such as its reward-driven adaptability, state representations, temporal structures and generalizability. In this survey, we present an overview of recent advancements of reinforcement learning and bandits, and discuss how they can be effectively employed to solve speech and natural language processing problems with models that are adaptive, interactive and scalable.
