Coherence Modeling

Coherence modeling focuses on identifying and quantifying the relationships between different parts of data, whether it's the causal links in a dynamical system, the alignment of modalities in a video, or the logical flow in a text. Current research explores this across diverse domains, employing techniques like contrastive learning, graph convolutional networks, and hierarchical modeling to improve coherence in various applications, including neural network training, video topic segmentation, and extractive summarization. These advancements have significant implications for improving the accuracy and interpretability of models in numerous fields, ranging from neuroscience and robotics to natural language processing and medical diagnostics.