Table 1 shows that overall performance on the UMass Citation dataset using the embedded-state latent CRF (95.18) is marginally better than the baseline BiLSTM+CRF model (95.07).
examining the entities with the largest F1 score improvement in Table 2, we see that they are mostly within the VENUE section, which has longrange constraints with other sections, giving evidence of the model's ability to learn constraints from the citation dataset.