Where Do We Go from Here? Multi-scale Allocentric Relational Inference
  from Natural Spatial Descriptions
Tzuf Paz-Argaman Sayali Kulkarni John Palowitch Jason Baldridge Reut Tsarfaty
When communicating routes in natural language, the concept of {\em acquired
spatial knowledge} is crucial for geographic information retrieval (GIR) and in
spatial cognitive research. However, NLP navigation studies often overlook the
impact of such acquired knowledge on textual descriptions. Current navigation
studies concentrate on egocentric local descriptions (e.g., `it will be on your
right') that require reasoning over the agent's local perception. These
instructions are typically given as a sequence of steps, with each action-step
explicitly mentioning and being followed by a landmark that the agent can use
to verify they are on the right path (e.g., `turn right and then you will
see...'). In contrast, descriptions based on knowledge acquired through a map
provide a complete view of the environment and capture its overall structure.
These instructions (e.g., `it is south of Central Park and a block north of a
police station') are typically non-sequential, contain allocentric relations,
with multiple spatial relations and implicit actions, without any explicit
verification. This paper introduces the Rendezvous (RVS) task and dataset,
which includes 10,404 examples of English geospatial instructions for reaching
a target location using map-knowledge. Our analysis reveals that RVS exhibits a
richer use of spatial allocentric relations, and requires resolving more
spatial relations simultaneously compared to previous text-based navigation
benchmarks.