  In this work, we propose the marginal structured SVM (MSSVM) for structured
prediction with hidden variables. MSSVM properly accounts for the uncertainty
of hidden variables, and can significantly outperform the previously proposed
latent structured SVM (LSSVM; Yu & Joachims (2009)) and other state-of-art
methods, especially when that uncertainty is large. Our method also results in
a smoother objective function, making gradient-based optimization of MSSVMs
converge significantly faster than for LSSVMs. We also show that our method
consistently outperforms hidden conditional random fields (HCRFs; Quattoni et
al. (2007)) on both simulated and real-world datasets. Furthermore, we propose
a unified framework that includes both our and several other existing methods
as special cases, and provides insights into the comparison of different models
in practice.
