Abstract: In various examples, techniques for training and using a task-oriented dialogue system are described. Systems and methods are disclosed for determining, using a prompt model(s) and based at least in part on text data, prompt data representing one or more prompts. Additionally, systems and method are disclosed for determining, using a language model(s) and based at least in part on the text data and the prompt data, a canonical form associated with the text data. In some examples, the prompt model(s) is trained to generate the prompt data that causes the language model(s) to output the canonical form. Systems and method are further disclosed for using the canonical form to determine at least an intent associated with the text data. A dialogue manager may then use the intent to perform one or more actions associated with the text data.

Abstract: An arrangement and corresponding method are described for natural language processing. A natural language understanding (NLU) arrangement processes a natural language input to determine a corresponding sentence-level interpretation. A user state component maintains user context data that characterizes an operating context of the NLU arrangement. Operation of the NLU arrangement is biased by the user context data.

Abstract: An arrangement and corresponding method are described for natural language processing. A natural language understanding (NLU) arrangement processes a natural language input to determine a corresponding sentence-level interpretation. A user state component maintains user context data that characterizes an operating context of the NLU arrangement. Operation of the NLU arrangement is biased by the user context data.