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Despite the recent emergence of video captioning models, how to generatevivid, fine-grained video descriptions based on the background knowledge (i.e.,long and informative commentary about the domain-specific scenes withappropriate reasoning) is still far from being solved, which however has greatapplications such as automatic sports narrative. In this paper, we presentGOAL, a benchmark of over 8.9k soccer video clips, 22k sentences, and 42kknowledge triples for proposing a challenging new task setting asKnowledge-grounded Video Captioning (KGVC). Moreover, we conduct experimentaladaption of existing methods to show the difficulty and potential directionsfor solving this valuable and applicable task. Our data and code are availableat https://github.com/THU-KEG/goal.

GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for Real-time Soccer Commentary Generation