  We investigate video-aided grammar induction, which learns a constituency
parser from both unlabeled text and its corresponding video. Existing methods
of multi-modal grammar induction focus on learning syntactic grammars from
text-image pairs, with promising results showing that the information from
static images is useful in induction. However, videos provide even richer
information, including not only static objects but also actions and state
changes useful for inducing verb phrases. In this paper, we explore rich
features (e.g. action, object, scene, audio, face, OCR and speech) from videos,
taking the recent Compound PCFG model as the baseline. We further propose a
Multi-Modal Compound PCFG model (MMC-PCFG) to effectively aggregate these rich
features from different modalities. Our proposed MMC-PCFG is trained end-to-end
and outperforms each individual modality and previous state-of-the-art systems
on three benchmarks, i.e. DiDeMo, YouCook2 and MSRVTT, confirming the
effectiveness of leveraging video information for unsupervised grammar
induction.
