论文标题
用多头注意机制翻译行为机器人导航的自然语言指令
Translating Natural Language Instructions for Behavioral Robot Navigation with a Multi-Head Attention Mechanism
论文作者
论文摘要
我们提出了一种多头注意机制作为神经网络模型中的混合层，将自然语言转化为室内机器人导航的高级行为语言。我们遵循（Zang等，2018a）建立的框架，该框架建议使用导航图作为任务的知识库。我们的结果表明，在翻译有关以前看不见的环境的说明时的性能获得了显着的提高，因此，提高了模型的概括能力。
We propose a multi-head attention mechanism as a blending layer in a neural network model that translates natural language to a high level behavioral language for indoor robot navigation. We follow the framework established by (Zang et al., 2018a) that proposes the use of a navigation graph as a knowledge base for the task. Our results show significant performance gains when translating instructions on previously unseen environments, therefore, improving the generalization capabilities of the model.
