Bethe Projections for Non-Local Inference
Table 1: Comparison of F1 scores on Citation Extraction dataset. We compare MAP inference F1 scores of our non-local energy model and the specialized dual decomposition model of Anzaroot et al. (2014). Both variants learn global regularities that significantly improve performance.
['Model', 'F1']
We can see that our baseline chain has slightly higher accuracy than the baseline approach of Anzaroot et al. Our augmented model (Non-Local Energies) matches and very slightly beats their soft dual decomposition (Soft-DD) procedure. This is especially impressive because they employ a specialized linear-programming solver and learning algorithm adapted to the task of MAP inference under hinge-loss soft constraints, whereas we simply plug in our general learning and inference algorithms for non-local structured prediction – applicable to any set of energy functions.