(ii) Based on the learned feature representations from Bi-LSTM, two conditional random field (CRF) models are performed for sequence labeling for source and target domain separately, where parameter transfer learning is performed.
an upper bound of KL divergence between the source and target domain's CRF label distributions is added over the emission and transition matrices across the source and target CRF models to explore the shareable parts of the parameters.