We address the problem of automatically aligning natural language sentences with corresponding video segments without any direct supervision. Most existing algorithms for integrating language with videos rely on handaligned parallel data, where each natural language sentence is manually aligned with its corresponding image or video segment. Recently, fully unsupervised alignment of text with video has been shown to be feasible using hierarchical generative models. In contrast to the previous generative models, we propose three latent-variable discriminative models for the unsupervised alignment task. The proposed discriminative models are capable of incorporating domain knowledge, by adding diverse and overlapping features. The results show that discriminative models outperform the generative models in terms of alignment accuracy.
REF utilize latent-variable based discriminative models (CRF, Structured Perceptron) for unsupervised alignment.
Discriminative Unsupervised Alignment of Natural Language Instructions with Corresponding Video Segments
HLT-NAACL
Computer Science