Concept Transfer Learning for Adaptive Language Understanding
Table 4: The performance of our methods evaluated on the DSTC3_S_test.
['Model', 'Training set', '[ITALIC] F1-score']
We can see that: 1) By incorporating the data of the source domain (dstc2_train), PS and AC achieve improvements respectively. 2) AC gains more than PS by modeling the plain semantic slot as atomic concepts. The atomic concepts promote the associated slots to share input features for the same atoms. 3) The atomic-concepts based slot filling considering the concepts dependently (ACD) gains little (0.17%) over AC considering the concepts independently. It may be due to the small size of dstc3_seed.