  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.
