Do Sentence Transformers Learn Quasi-Geospatial Concepts from General Text?
<|reference_start|>Do Sentence Transformers Learn Quasi-Geospatial Concepts from General Text?: 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.<|reference_end|>
@article{ilyankou2024do,
    title={Do Sentence Transformers Learn Quasi-Geospatial Concepts from General
  Text?},
    author={Ilya Ilyankou, Aldo Lipani, Stefano Cavazzi, Xiaowei Gao and James
  Haworth},
    journal={arXiv preprint arXiv:2404.04169},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04169},
    primaryClass={cs.CL cs.LG}
}