  Language understanding is essential for the navigation agent to follow
instructions. We observe two kinds of issues in the instructions that can make
the navigation task challenging: 1. The mentioned landmarks are not
recognizable by the navigation agent due to the different vision abilities of
the instructor and the modeled agent. 2. The mentioned landmarks are applicable
to multiple targets, thus not distinctive for selecting the target among the
candidate viewpoints. To deal with these issues, we design a translator module
for the navigation agent to convert the original instructions into
easy-to-follow sub-instruction representations at each step. The translator
needs to focus on the recognizable and distinctive landmarks based on the
agent's visual abilities and the observed visual environment. To achieve this
goal, we create a new synthetic sub-instruction dataset and design specific
tasks to train the translator and the navigation agent. We evaluate our
approach on Room2Room~(R2R), Room4room~(R4R), and Room2Room Last (R2R-Last)
datasets and achieve state-of-the-art results on multiple benchmarks.
