Domain Adaptation of Multilingual Semantic Search -- Literature Review
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909093544853504 |
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| author | Bringmann, Anna Zhukova, Anastasia |
| author_facet | Bringmann, Anna Zhukova, Anastasia |
| contents | This literature review gives an overview of current approaches to perform domain adaptation in a low-resource and approaches to perform multilingual semantic search in a low-resource setting. We developed a new typology to cluster domain adaptation approaches based on the part of dense textual information retrieval systems, which they adapt, focusing on how to combine them efficiently. We also explore the possibilities of combining multilingual semantic search with domain adaptation approaches for dense retrievers in a low-resource setting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02932 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Domain Adaptation of Multilingual Semantic Search -- Literature Review Bringmann, Anna Zhukova, Anastasia Information Retrieval Machine Learning This literature review gives an overview of current approaches to perform domain adaptation in a low-resource and approaches to perform multilingual semantic search in a low-resource setting. We developed a new typology to cluster domain adaptation approaches based on the part of dense textual information retrieval systems, which they adapt, focusing on how to combine them efficiently. We also explore the possibilities of combining multilingual semantic search with domain adaptation approaches for dense retrievers in a low-resource setting. |
| title | Domain Adaptation of Multilingual Semantic Search -- Literature Review |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2402.02932 |