KERNEL DEEP CONVEX NETWORKS AND END-TO-END LEARNING

摘要

Data associated with spoken language may be obtained. An analysis of the obtained data may be initiated for understanding of the spoken language using a deep convex network that is integrated with a kernel trick. The resulting kernel deep convex network may also be constructed by stacking one shallow kernel network over another with concatenation of the output vector of the lower network with the input data vector. A probability associated with a slot that is associated with slot-filling may be determined, based on local, discriminative features that are extracted using the kernel deep convex network.

主权项

1. A system comprising:
a device that includes at least one processor, the device including a language understanding engine comprising instructions tangibly embodied on a computer readable storage medium for execution by the at least one processor, the language understanding engine including:
a feature acquisition component configured to obtain local, discriminative features that are associated with an input spoken language string;a slot-filling component configured to determine a plurality of probabilities associated with a plurality of respective slots that are associated with a slot-filling task in spoken language understanding (SLU);a softmax interface configured to provide an interface between the feature acquisition component and the slot-filling component, using a softmax function; andan end-to-end learning component configured to train parameters for the softmax interface, based on an objective function taking a value of a model-based expectation of slot-filling accuracy over an entire training set.