Improving Long Distance Slot Carryover in Spoken Dialogue Systems
Table 4: Carryover performance (F1) of different models for slots at different distances on DSTC2 dataset.
['Decoder', 'Slot Encoder', 'Slot Ordering', 'Slot distance  [BOLD] 0', 'Slot distance  [BOLD] 2', 'Slot distance  [BOLD] 4', 'Slot distance ≥ [BOLD] 6']
S4SS3SSS0Px3 Impact of decoder Compared to the baseline model, both the pointer network model and the transformer model are able to carry over longer dialogue context due to being able to model the slot interdependence. With the transformer network, we completely forgo ordering information. Though the slot embedding includes distance feature \bfxdist, the actual order in which the slots are arranged does not matter. We see improvement in carryover performance for slots at all distances. While the pointer network seems to deal with longer context better, the transformer architecture still gives us the best overall performance.