Video-aided grammar induction aims to leverage video information for finding
more accurate syntactic grammars for accompanying text. While previous work
focuses on building systems for inducing grammars on text that are well-aligned
with video content, we investigate the scenario, in which text and video are
only in loose correspondence. Such data can be found in abundance online, and
the weak correspondence is similar to the indeterminacy problem studied in
language acquisition. Furthermore, we build a new model that can better learn
video-span correlation without manually designed features adopted by previous
work. Experiments show that our model trained only on large-scale YouTube data
with no text-video alignment reports strong and robust performances across
three unseen datasets, despite domain shift and noisy label issues. Furthermore
our model yields higher F1 scores than the previous state-of-the-art systems
trained on in-domain data.

本文研究了视频和文字间松散对应情况下的视频辅助语法归纳方法，构建了新的模型并在大规模 YouTube 数据上进行训练，实现了在不同数据集上的强大性能表现，优于之前领先的系统。