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MultiVENT: Multilingual Videos of Events with Aligned Natural Text

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arxiv 2307.03153 v1 pith:QAJLXQYA submitted 2023-07-06 cs.IR cs.CVcs.MM

Kate Sanders , David Etter , Reno Kriz , Benjamin Van Durme This is my paper · ORCID
classification cs.IRcs.CVcs.MM
keywords newsmultilingualmultiventvideosvideobroadcastsdatasetsfootage
verification ladder T0 review T1 audit T2 compute T3 formal
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Everyday news coverage has shifted from traditional broadcasts towards a wide range of presentation formats such as first-hand, unedited video footage. Datasets that reflect the diverse array of multimodal, multilingual news sources available online could be used to teach models to benefit from this shift, but existing news video datasets focus on traditional news broadcasts produced for English-speaking audiences. We address this limitation by constructing MultiVENT, a dataset of multilingual, event-centric videos grounded in text documents across five target languages. MultiVENT includes both news broadcast videos and non-professional event footage, which we use to analyze the state of online news videos and how they can be leveraged to build robust, factually accurate models. Finally, we provide a model for complex, multilingual video retrieval to serve as a baseline for information retrieval using MultiVENT.

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  1. MAGMaR Shared Task System Description: Video Retrieval with OmniEmbed

    cs.IR 2025-06 conditional novelty 4.0 of 10

    Fine-tuning OmniEmbed on MultiVENT 2.0 with text, audio, and video inputs yields the best public MAGMaR video retrieval results, with non-text signals matching text-only performance.

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