Amazon Technologies, Inc.

Archit Jain

Joe Pemberton

Michael F Schwartz

Alexander Say Go

Vijitha Raji

Chenlei Guo

Xing Fan

Dhruva Lakshmana Rao Batni

Kerry Hammil

Alexander Thomas Loeb

Steven Mack Saunders

Hyungseo Park

Tara Raj

Robert O'Neal Copeland

Devices and techniques are generally described for arbitration between LLM-based and intent-based natural language processing flows. In various examples, an automatic speech recognition (ASR) component may generate first ASR output data representing the first natural language input. A first machine learning model may select an LLM-based natural language processing flow. The first machine learning model may be trained to select between at least the LLM-based and non-LLM-based natural language processing flows. The first ASR output data may be processed using the LLM-based processing flow. The LLM-based processing flow may generate first executable data.

Language model arbitration for natural language processing

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.

Natural language input routing

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.

Natural language processing routing

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.

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Archit Jain