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arXiv · 2404.04169

Do Sentence Transformers Learn Quasi-Geospatial Concepts from General Text?

Ilya Ilyankou·Aldo Lipani·Stefano Cavazzi·Xiaowei Gao·James Haworth
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Abstract

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.

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Ilya Ilyankou, Aldo Lipani, Stefano Cavazzi, Xiaowei Gao, James Haworth. 2024-04-05. Do Sentence Transformers Learn Quasi-Geospatial Concepts from General Text?. https://arxiv.org/abs/2404.04169

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