Quantifying the Gaps Between Translation and Native Perception in Training for Multimodal, Multilingual Retrieval
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914967817551872 |
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| author | Buettner, Kyle Kovashka, Adriana |
| author_facet | Buettner, Kyle Kovashka, Adriana |
| contents | There is a scarcity of multilingual vision-language models that properly account for the perceptual differences that are reflected in image captions across languages and cultures. In this work, through a multimodal, multilingual retrieval case study, we quantify the existing lack of model flexibility. We empirically show performance gaps between training on captions that come from native German perception and captions that have been either machine-translated or human-translated from English into German. To address these gaps, we further propose and evaluate caption augmentation strategies. While we achieve mean recall improvements (+1.3), gaps still remain, indicating an open area of future work for the community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_02027 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantifying the Gaps Between Translation and Native Perception in Training for Multimodal, Multilingual Retrieval Buettner, Kyle Kovashka, Adriana Computer Vision and Pattern Recognition Artificial Intelligence There is a scarcity of multilingual vision-language models that properly account for the perceptual differences that are reflected in image captions across languages and cultures. In this work, through a multimodal, multilingual retrieval case study, we quantify the existing lack of model flexibility. We empirically show performance gaps between training on captions that come from native German perception and captions that have been either machine-translated or human-translated from English into German. To address these gaps, we further propose and evaluate caption augmentation strategies. While we achieve mean recall improvements (+1.3), gaps still remain, indicating an open area of future work for the community. |
| title | Quantifying the Gaps Between Translation and Native Perception in Training for Multimodal, Multilingual Retrieval |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2410.02027 |