30
19

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

Ji Qi
Jifan Yu
Teng Tu
Kunyu Gao
Yifan Xu
Xinyu Guan
Xiaozhi Wang
Yuxiao Dong
Bin Xu
Lei Hou
Juanzi Li
Jie Tang
Weidong Guo
Hui Liu
Yu-Syuan Xu
Abstract

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

View on arXiv
Comments on this paper