Bethe Projections for Non-Local Inference
Table 4: Character-wise accuracy of our baselines, and models using learned non-local energies on Handwriting Recognition dataset. Note that word classifier baseline is also given in character-wise accuracy for comparison.
['Model', 'Accuracy']
Adding the non-local energy brings our performance well above the baseline bigram chain model, and our training procedure is able to give substantially better performance when ψ depends on the above input features. Of course, since the dataset has only 55 actual labels, and some of those are not valid for different input sequences due to length mismatches, this is arguably a classification problem as much as a structured prediction problem. To address this, we create another baseline, which is a constrained 55-class logistic regression classifier (constrained to only allow choosing output classes with appropriate lengths given the input). We also tune the number of random Fourier features as a hyperparameter to give the classifier as much expressive power as possible. As we can see, the performance is still significantly below the best structured models, indicating that the interplay between local and global structure is important.