Do Sentence Transformers Learn Quasi-Geospatial Concepts from General
  Text?
Ilya Ilyankou Aldo Lipani Stefano Cavazzi Xiaowei Gao James Haworth
Sentence transformers are language models designed to perform semantic
search. This study investigates the capacity of sentence transformers,
fine-tuned on general question-answering datasets for asymmetric semantic
search, to associate descriptions of human-generated routes across Great
Britain with queries often used to describe hiking experiences. We find that
sentence transformers have some zero-shot capabilities to understand
quasi-geospatial concepts, such as route types and difficulty, suggesting their
potential utility for routing recommendation systems.