MERLIN: A Testbed for Multilingual Multimodal Entity Recognition and Linking
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908596863762432 |
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| author | Ramamoorthy, Sathyanarayanan Shah, Vishwa Khanuja, Simran Sheikh, Zaid Jie, Shan Chia, Ann Chua, Shearman Neubig, Graham |
| author_facet | Ramamoorthy, Sathyanarayanan Shah, Vishwa Khanuja, Simran Sheikh, Zaid Jie, Shan Chia, Ann Chua, Shearman Neubig, Graham |
| contents | This paper introduces MERLIN, a novel testbed system for the task of Multilingual Multimodal Entity Linking. The created dataset includes BBC news article titles, paired with corresponding images, in five languages: Hindi, Japanese, Indonesian, Vietnamese, and Tamil, featuring over 7,000 named entity mentions linked to 2,500 unique Wikidata entities. We also include several benchmarks using multilingual and multimodal entity linking methods exploring different language models like LLaMa-2 and Aya-23. Our findings indicate that incorporating visual data improves the accuracy of entity linking, especially for entities where the textual context is ambiguous or insufficient, and particularly for models that do not have strong multilingual abilities. For the work, the dataset, methods are available here at https://github.com/rsathya4802/merlin |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14307 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MERLIN: A Testbed for Multilingual Multimodal Entity Recognition and Linking Ramamoorthy, Sathyanarayanan Shah, Vishwa Khanuja, Simran Sheikh, Zaid Jie, Shan Chia, Ann Chua, Shearman Neubig, Graham Computation and Language Artificial Intelligence This paper introduces MERLIN, a novel testbed system for the task of Multilingual Multimodal Entity Linking. The created dataset includes BBC news article titles, paired with corresponding images, in five languages: Hindi, Japanese, Indonesian, Vietnamese, and Tamil, featuring over 7,000 named entity mentions linked to 2,500 unique Wikidata entities. We also include several benchmarks using multilingual and multimodal entity linking methods exploring different language models like LLaMa-2 and Aya-23. Our findings indicate that incorporating visual data improves the accuracy of entity linking, especially for entities where the textual context is ambiguous or insufficient, and particularly for models that do not have strong multilingual abilities. For the work, the dataset, methods are available here at https://github.com/rsathya4802/merlin |
| title | MERLIN: A Testbed for Multilingual Multimodal Entity Recognition and Linking |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2510.14307 |