Concept Transfer Learning for Adaptive Language Understanding
Table 2: Comparison with the published results on the standard ATIS task, and evaluation on ATIS_X_test. (∗ denotes our implementation.)
['Model', 'ATIS', 'ATIS_X_test']
We can see that RNN outperforms CRF because of the ability to capture long-term dependencies. LSTM beats RNN by solving the problem of vanishing or exploding gradients. BLSTM further improves the result by considering both the past and future features. Encoder-decoder achieves the state-of-the-art performance by modeling the label dependencies. Encoder-labeler is a similar method to the Encoder-decoder. These systems are designed to predict the plain semantic slots traditionally. AC gets a marginal improvement (+0.15%) over PS by predicting the atomic concepts independently instead of the plain slots. Moreover, ACD predicts the atomic concepts dependently, gains 0.50% (significant level 95%) over the AC. Our methods are also tested on the ATIS_X_test to measure the ability of generalization. 2) The atomic-concepts based slot filling gets a slight improvement over the PS with dict-feats, considering the concepts independently (AC). 3) The atomic-concepts based slot fillings (ACD gains a large margin over AC, considering the concepts dependently. 4)