Google Inc.

Chinnadhurai Sankar

Ben David Goodrich

Daniel Duckworth

Vishaal Prasad

Arvind Neelakantan

Semih Yavuz

Sujith Ravi

Zornitsa Kozareva

Training and/or utilizing a single neural network model to generate, at each of a plurality of assistant turns of a dialog session between a user and an automated assistant, a corresponding automated assistant natural language response and/or a corresponding automated assistant action. For example, at a given assistant turn of a dialog session, both a corresponding natural language response and a corresponding action can be generated jointly and based directly on output generated using the single neural network model. The corresponding response and/or corresponding action can be generated based on processing, using the neural network model, dialog history and a plurality of discrete resources. For example, the neural network model can be used to generate a response and/or action on a token-by-token basis.

Generating automated assistant responses and/or actions directly from dialog history and resources

Systems and methods are provided to pre-train projection networks for use as transferable natural language representation generators. In particular, example pre-training schemes described herein enable learning of transferable deep neural projection representations over randomized locality sensitive hashing (LSH) projections, thereby surmounting the need to store any embedding matrices because the projections can be dynamically computed at inference time.

Pre-trained projection networks for transferable natural language representations

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Montreal, Canada

Chinnadhurai Sankar