Abstract: Techniques for selecting a skill component to process a natural language input are described. When a natural language input is received, natural language understanding (NLU) output data representing the natural language input is generated, and skill components (capable of processing the NLU output data) are determined. Thereafter, rules (for preventing the invocation of skill components) are implemented in a tiered manner, resulting in the determination of a subset of the skill components. The subset of skill components is ranked using a machine learning model(s), and the top-ranked skill component is called to process the NLU output data.

Abstract: A computer-implemented method for classifying for classifying input record data by relevance to classification options of a classification scheme. The input record data comprising a plurality of input records, each input record comprising one or more record features. The method comprises: receiving a set of relevance scores based on first and second classification techniques, the set of relevance scores comprising pairs of relevance scores, each pair of relevance scores being associated with a respective record feature and a respective classification option and comprising a first relevance score obtained by the first classification technique and a second relevance score obtained by the second classification technique, each of the first and second relevance scores being indicative of a relevance of the respective record feature to the respective classification option; determining one or more ambiguous record features of the record features by comparing the first and second relevance scores.

Abstract: Devices and techniques are generally described for a speech processing routing architecture. First input data representing an input request may be received. First data may be sent to a first skill representing a first request for the first skill to evaluate an ability of the first skill to process the first input data. Second data may be sent to a second skill representing a second request for the second skill to evaluate an ability of the second skill to process the first input data. Third data may be received from the first skill indicating a first action performed by the first skill in response to receipt of the first input data. Fourth data may be received from the second skill indicating a second action performed by the second skill. The first skill may be selected for processing the first input data.

Abstract: Techniques for performing runtime ranking of skill components are described. A skill developer may generate a rule indicating a skill component is to be invoked at runtime when a natural language input corresponds to a specific context. At runtime, a virtual assistant system may implement a machine learned model to generate an initial ranking of skill components. Thereafter, the virtual assistant system may use skill component-specific rules to adjust the initial ranking, and this second ranking is used to determine which skill component to invoke to respond to the natural language input. Overtime, if a rule results in beneficial user experiences, the virtual assistant system may incorporate the rule into the machine learned model.