Data Augmentation with Atomic Templates for Spoken Language Understanding
Table 3: SLU performances on the DSTC3 evaluation set when removing different modules of our method.
['[BOLD] Method', '[BOLD] F1-score']
Ablation Study By removing SLU model pretraining on DSTC2 (“- dstc2”) and finetuning on the seed data (“- dstc3_seed”), we can see a significant decrease in SLU performance. When we subsequently cast aside the sentence generator (“- sentence generator”, i.e. using the atomic exemplars as inputs of SLU model directly), the SLU performance decreases by 10.3%. This shows that the sentence generator can produce more natural utterances. If we replace the atomic exemplars as the corresponding act-slot-value triples (“- atomic templates”), the SLU performance drops sharply. The reason may be that the atomic templates provide a better description of corresponding semantic meanings than the surfaces of the triples.