Data Augmentation for Spoken Language Understanding via Joint Variational Generation
Table 4: Comparisons of the best slot filling and intent detection results for the ATIS dataset.
['Dataset', 'Model', 'Slot (F1)', 'Intent (F1)']
We found the model suitable for our task, as the model is reasonably complex and distinctive from our simple baseline. Furthermore, the code for running the model is publicly available and the results are readily reproducible. We were able to obtain similar or even better results on our environment This difference might be due to differing data preprocessing methods. SLU performance is measure by (1) slot filling f1-score (evaluated using the conlleval perl script), (2) intent identification f1-sore, and (3) semantic frame formulation. f1-score measures the correctness of predicted slot labels. In the best case, our approach was able to boost the slot filling performance for the slot-gated (full) model by 0.38. Remarkably, our best results outpeformed more complex models, further supporting the idea of data-centric regularization. We also evaluate the SLU performance of JLUVA by performing deterministic inference (i.e. z=μ). We find that the LU performance by itself is not competitive. This eliminates the possibility that the performance gains in our approach are attributed to JLUVA being a more expressive model and therefore acting as a teacher network.