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.

通过设计一个翻译模块，将原有语句转化为易于理解的子语句，该模块针对航行代理的视觉能力和环境观察结果，聚焦于可识别和有区分性的地标，通过训练翻译器和导航代理的特定任务，达到了在多个基准测试中取得最新颖成果的目标。