Efficient Exact Inference With Loss Augmented Objective in Structured Learning

Structural support vector machine (SVM) is an elegant approach for building complex and accurate models with structured outputs. However, its applicability relies on the availability of efficient inference algorithms—the state-of-the-art training algorithms repeatedly perform inference to compute a subgradient or to find the most violating configuration. In this paper, we propose an exact inference algorithm for maximizing nondecomposable objectives due to special type of a high-order potential having a decomposable internal structure. As an important application, our method covers the loss augmented inference, which enables the <italic>slack</italic> and <italic>margin</italic> scaling formulations of structural SVM with a variety of <italic>dissimilarity measures</italic>, e.g., Hamming loss, precision and recall, <inline-formula> <tex-math notation="LaTeX">$F_{\beta }$ </tex-math></inline-formula>-loss, intersection over union, and many other functions that can be efficiently computed from the contingency table. We demonstrate the advantages of our approach in natural language parsing and sequence segmentation applications.