Do Sentence Transformers Learn Quasi-Geospatial Concepts from General Text?
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866918485372698624 |
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| author | Ilyankou, Ilya Lipani, Aldo Cavazzi, Stefano Gao, Xiaowei Haworth, James |
| author_facet | Ilyankou, Ilya Lipani, Aldo Cavazzi, Stefano Gao, Xiaowei Haworth, James |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_04169 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Do Sentence Transformers Learn Quasi-Geospatial Concepts from General Text? Ilyankou, Ilya Lipani, Aldo Cavazzi, Stefano Gao, Xiaowei Haworth, James Computation and Language Machine Learning 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. |
| title | Do Sentence Transformers Learn Quasi-Geospatial Concepts from General Text? |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2404.04169 |