  One of the most challenging topics in Natural Language Processing (NLP) is
visually-grounded language understanding and reasoning. Outdoor
vision-and-language navigation (VLN) is such a task where an agent follows
natural language instructions and navigates a real-life urban environment. Due
to the lack of human-annotated instructions that illustrate intricate urban
scenes, outdoor VLN remains a challenging task to solve. This paper introduces
a Multimodal Text Style Transfer (MTST) learning approach and leverages
external multimodal resources to mitigate data scarcity in outdoor navigation
tasks. We first enrich the navigation data by transferring the style of the
instructions generated by Google Maps API, then pre-train the navigator with
the augmented external outdoor navigation dataset. Experimental results show
that our MTST learning approach is model-agnostic, and our MTST approach
significantly outperforms the baseline models on the outdoor VLN task,
improving task completion rate by 8.7% relatively on the test set.
