论文标题
在上下文中了解自然语言
Understanding Natural Language in Context
论文作者
论文摘要
近年来，越来越多的应用程序具有自然语言接口，无论是聊天机器人的形式还是通过个人助理（例如Alexa（Amazon），Google Assistant，Siri（Apple）和Cortana（Microsoft））等个人助理。要使用这些应用程序，需要在机器人和人之间进行基本对话。 尽管今天存在这种对话框，主要存在于“静态”机器人内，这些机器人不会在家庭空间中进行任何运动，但在处理可以在我们家庭环境中移动和操纵物体的机器人时，对环境传达信息的推理的挑战大大增加。 在本文中，我们专注于认知机器人，这些机器人具有一些基于知识的世界模型，并通过这种模型通过推理和计划来运作。因此，当机器人和人类交流时，已经可以使用一些形式主义 - 机器人的知识代表形式主义。 我们在这项研究中的目标是将自然语言转化为该机器人的形式主义，从而完成更复杂的家庭任务。我们通过结合现成的SOTA语言模型，计划工具和机器人的知识库来做到这一点，以更好地进行沟通。此外，我们分析了不同的指令类型，并说明了世界上下文对翻译过程的贡献。
Recent years have seen an increasing number of applications that have a natural language interface, either in the form of chatbots or via personal assistants such as Alexa (Amazon), Google Assistant, Siri (Apple), and Cortana (Microsoft). To use these applications, a basic dialog between the robot and the human is required. While this kind of dialog exists today mainly within "static" robots that do not make any movement in the household space, the challenge of reasoning about the information conveyed by the environment increases significantly when dealing with robots that can move and manipulate objects in our home environment. In this paper, we focus on cognitive robots, which have some knowledge-based models of the world and operate by reasoning and planning with this model. Thus, when the robot and the human communicate, there is already some formalism they can use - the robot's knowledge representation formalism. Our goal in this research is to translate natural language utterances into this robot's formalism, allowing much more complicated household tasks to be completed. We do so by combining off-the-shelf SOTA language models, planning tools, and the robot's knowledge-base for better communication. In addition, we analyze different directive types and illustrate the contribution of the world's context to the translation process.
