Dialogue history integration into end-to-end signal-to-concept spoken language understanding systems
Table 2: Slot filling performance results on the MEDIA test dataset for the baseline model and models trained with different types of dialog history embedding vectors. Results are given in terms of CER and CVER metrics (%); ΔCER and ΔCVER (%) denote relative error reduction for CER and CVER correspondingly in comparison with the baseline model (#1).
['[BOLD] #', '[BOLD] h-vector type', '[BOLD] CER', 'Δ [BOLD] CER', '[BOLD] CVER', 'Δ [BOLD] CVER']
All the models in this table are trained directly on the MEDIA training corpus. The first line shows the baseline result for the end-to-end signal-to-concept model. The other three lines (#2,3,4) correspond to the models trained with dialog history integration and differ from each other in the way the dialog history is represented in the form of h-vectors. We can observe, that all types of h-vectors provide an improvement over the baseline model for both metrics CER and CVER. The best result (line #4) is obtained for supervised-all h-vectors and corresponds to 12.5% of relative CER reduction and to 11.9% of CVER reduction in comparison with the baseline model.