  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.
