By incorporating the bi-gram transition rules (Row 2), the joint teacher model q achieves 1.56 improvement in F1 score that outperforms most previous neural based methods (Rows 4-7), including the BLSTM-CRF model (Lample et al., 2016) which applies a conditional random field (CRF) on top of a BLSTM in order to capture the transition patterns and encourage valid sequences.
our method implements the desired constraints in a more straightforward way by using the declarative logic rule language, and at the same time does not introduce extra model parameters to learn.