Learning Inference Networks for Structured Prediction Energy Networks
Table 8: Development F1 for Bookmarks when comparing hinge losses for SPEN (InfNet) and whether to retune the inference network.
['hinge loss', '-retuning', '+retuning']
Comparison of Loss Functions and Impact of Inference Network Retuning. We find performance to be highly similar across the losses, with the contrastive loss appearing slightly better than the others. After training, we “retune” the inference network as specified by Eq. We were surprised to see that the final cost-augmented inference network performs well as a test-time inference network. This suggests that by the end of training, the cost-augmented network may be approaching the argmin and that there may not be much need for retuning.