Google Inc.

Xiaoxue Zang

Abhinav Kumar Rastogi

Srinivas Kumar Sunkara

Blaise Hilary Aguera-Arcas

Jindong Chen

Zecheng He

Gabriel Overholt Schubiner

Nevan Holt Wichers

Lijuan Liu

Ying Xu

Raghav Gupta

Pranav Khaitan

Generally, the present disclosure is directed to user interface understanding. More particularly, the present disclosure relates to training and utilization of machine-learned models for user interface prediction and/or generation. A machine-learned interface prediction model can be pre-trained using a variety of pre-training tasks for eventual downstream task training and utilization (e.g., interface prediction, interface generation, etc.).

Machine-learned models for user interface prediction, generation, and interaction understanding

Generally, the present disclosure is directed to systems and methods for performing task-oriented response generation that can provide advantages for artificial intelligence systems or other computing systems that include natural language processing for interpreting user input. Example implementations can process natural language descriptions of various services that can be accessed by the system. In response to a natural language input, systems can identify relevant values for executing one of the service(s), based in part on comparing embedded representations of the natural language input and the natural language description using a machine learned model.

Schema-guided response generation

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Santa Clara, CA, United States of America

Xiaoxue Zang