A HIERARCHICAL DECODING MODEL FOR SPOKEN LANGUAGE UNDERSTANDING FROM UNALIGNED DATA
Table 3: SLU performance (F1) with 5% training data on two categories of labels (act-slot-value triples).
['Data Type', 'Label Type', 'ST', 'ZS', 'HD']
To better explore the reason why our model achieves much better performance than the baselines with only 5% data, we split the labels in the testing set into two categories according to whether the act-slot-value triple is seen in training set. We find that the decomposition of act-slot-value triples and hierarchical decoding of our method can generalize to unseen labels and improve the performance on seen labels simultaneously.