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SUMBT: Slot-Utterance Matching For Universal And Scalable Belief Tracking

The results are shown in Table 2. From the result of without slot consideration layer, 0.9% and 0.7% total acc drops on SNIPS and ATIS dataset, respectively. For examples, on Sim-M dataset, our model achieves an absolute improvement of 5.3% in contrast with BERT-DST. This permits the model to study different sorts of items, resembling …

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