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Since these slot values are extra likely to look in the form of unknown and advanced representation in observe, the outcomes of our mannequin display that our mannequin also has a fantastic potential in practical utility.
When coping with more advanced unknown slot values, STN4DST presents higher generalization and scalability than the widely used span extraction, displaying higher research potential and utility prospect. To investigate the restrictions of our mannequin in terms of generalization capacity, we manually replace all unenumerated slot values in the take a look at set of Sim-M and Sim-R with unknown slot values (manually written but meaningful).
Above results absolutely show the advantages of our mannequin, particularly for Sim-M and Sim-R, both of which pose nice challenges due to the big number of unknown slot values. SpanPtr (Xu and Hu 201 encodes the whole dialogue historical past with a bidirectional LSTM and extracts slot value for each slot by generating the beginning and end positions in dialogue historical past.

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