Here, we extend the model by incorporating an auto-encoder loss taking hand-crafted features as in/output, thereby forcing the model to preserve crucial information stored in such features and allowing us to evaluate the impacts of each feature on model performance.
our model, referred to as Neural-CRF+AE, consists of four major components: (1) a character-level CNN (char-CNN); (2) a word-level bi-directional LSTM (Bi-LSTM); (3) a conditional random field (CRF); and (4) an auto-encoder auxiliary loss.