Data Augmentation with Atomic Templates for Spoken Language Understanding
Table 2: SLU performances of different systems on the DSTC3 evaluation set.
['[BOLD] SLU', '[BOLD] Augmentation', '[BOLD] F1-score']
We can see that: The hierarchical decoding (HD) model gets better performance than the zero-shot learning (ZS) method of SLU. The seed data dstc3_seed limits the power of the SLU model, and even the naive augmentation can enhance it. Our data augmentation method with atomic templates (AT) improves the SLU performance dramatically. One reason may be the generated data has higher variety of semantic meaning than the naive augmentation. Combination can make up more dialogue acts and shows better result than Seed abridgement, while Seed abridgement provides more realistic dialogue acts. Thus, their union gives the best result. The best performance of our method is close to the human-designed sentence-level templates Zhu et al.