Abstract: A natural language understanding (NLU) framework includes an a concept system that performs concept matching of user utterances. The concept system generates a concept cluster model from sample utterances of an intent-entity model, and then trains a machine learning (ML) concept model based on the concept cluster model. Once trained, the concept model receives semantic vectors representing potential concepts extracted from utterances, and provides concept indicators to an ensemble scoring system. These concept indicators include indications of which concepts of the concept model that matched to the potential concepts, which intents of the intent-entity model are related to these concepts, and concept-relationship scores indicating a strength and/or uniqueness of the relationship between each concept-intent combination.

Abstract: One embodiment of the present invention sets forth a technique for managing machine learning. The technique includes organizing a set of reusable components for performing machine learning under a framework. The technique also includes representing, within the framework, a machine learning model as a graph-based structure that includes nodes representing a subset of the reusable components and edges representing input-output relationships between pairs of the nodes. The technique further includes validating the machine learning model based on inputs and outputs associated with the nodes and the input-output relationships represented by the edges in the graph-based structure. Finally, the technique includes generating the machine learning model according to the graph-based structure and configurations for the subset of the reusable components.

Abstract: One embodiment of the present invention sets forth a technique for processing training data for a machine learning model. The technique includes training the machine learning model using training data comprising a set of features and a set of original labels associated with the set of features. The technique also includes generating multiple groupings of the training data based on internal representations of the training data in the machine learning model. The technique further includes replacing, in a first subset of groupings of the training data, a first subset of the original labels with updated labels based at least on occurrences of values for the original labels in the first subset of groupings.

Abstract: There is provided a mechanism for attaching and detaching a portable cordless telephone in a motor vehicle, the mechanism comprising: a holder capable of holding the portable cordless telephone and pivotable from its inmost position in the mechanism to its maximum outwardly projected position; a spring device enabling the holder together with the portable cordless telephone to pivot towards their outwardly projected positions: a lock/unlock device provided for locking/unlocking the holder and the portable cordless telephone on the mechanism. When the holder and the portable cordless telephone are released from their locked conditions by an action of the lock/unlock device, the holder will be pressed by the above spring device, so that the holder together with the portable cordless telephone may pivot outwardly until the holder arrives at its maximum outwardly projected position.