Microsoft Technology Licensing, LLC

Semantic Machines, Inc.

Pengyu Chen

David Leo Wright Hall

Daniel Louis Klein

Laurence Steven Gillick

Adam David Pauls

Daniel Lawrence Roth

Jesse Daniel Eskes Rusak

Jordan Rian Cohen

Jacob Daniel Andreas

Michael Jack Newman

Andrew Volpe

Steven Andrew Wegmann

Taylor Darwin Berg-Kirkpatrick

Jayant Sivarama Krishnamurthy

Jason Andrew Wolfe

Jason Michael Eisner

Samuel McIntire Thomson

Emmanouil Antonios Platanios

Benjamin David Van Durme

Eui Chul Shin

Christopher H Lin

Subhro Roy

Benjamin Lev Snyder

Jordan R Cohen

Systems and methods are provided for automatically generating a program based on a natural language utterance using semantic parsing. The semantic parsing includes translating a natural language utterance into instructions in a logical form for execution. The methods use a pre-trained natural language model and generate a canonical utterance as an intermediate form before generating the logical form. The natural language model may be an auto-regressive natural language model with a transformer to paraphrase a sequence of words or tokens in the natural language utterance. The methods generate a prompt including exemplar input/output pairs as a few-shot learning technique for the natural language model to predict words or tokens. The methods further use constrained decoding to determine a canonical utterance, iteratively selecting sequence of words as predicted by the model against rules for canonical utterances. The methods generate a program based on the canonical utterance for execution in an application.

Semantic parsing of utterance using contractive paraphrasing

A method of leveraging a dialogue history of a conversational computing interface to execute an updated dialogue plan. The method comprises maintaining an annotated dialogue history of the conversational computing interface. The annotated dialogue history includes a plurality of traced steps defining a data-flow including input data used to execute a context-dependent operation and output data recorded from a previous execution of the context-dependent operation. The method further comprises recognizing an updated dialogue plan including a prefix of executable steps and an updated executable step following the prefix. The method further comprises automatically computer-recognizing that the prefix of executable steps of the updated dialogue plan matches a corresponding prefix of traced steps in the annotated dialogue history. The method further comprises re-using the data-flow from the prefix of traced steps in the annotated dialogue history to automatically determine input data of the updated executable step.

Leveraging dialogue history in updated dialogue

A method for configuring an automated dialogue system uses traces of interactions via a graphical user interface (GUI) for an application. Each trace includes interactions in the context of a plurality of presentations of the GUI. Elements of one or more presentations of the GUI are identified, and templates are associated with portions of the trace. Each template has one or more defined inputs and a defined output. For each template of the plurality of templates, the portions of the traces are processed to automatically configure the template by specifying a procedure for providing values of inputs to the template via the GUI and obtaining a value of an output. The automated dialogue system is configured with the configured templates, thereby avoiding manual configuration of the dialogue system.

Automatic spoken dialogue script discovery

A method for generating a dialogue event in a natural language processing system comprises loading, into a computer memory, a computer-readable seed command describing an ordered sequence of two or more top-level dialogue events. A dialogue event includes a client utterance or a computerized assistant response. The seed command includes one or more sub-commands, each sub-command corresponding to a portion of the ordered sequence of two or more top-level dialogue events, and the focal sub-command of the one or more sub-commands being parametrized by a seed semantic parameter. The method further comprises re-parametrizing the focal sub-command by outputting a plurality of different re-parametrized focal sub-commands wherein, in each re-parametrized focal sub-command, the seed semantic parameter is replaced by one of a plurality of different synthetic semantic parameters. The method further comprises, for each of the plurality of different synthetic semantic parameters: saving a corresponding re-parametrized focal sub-command.

Generating dialogue events for natural language system

A method for configuring an automated dialog system uses traces of interactions via a graphical user interface (GUI) for an application. Each trace includes interactions in the context of a plurality of presentations of the GUI. Elements of one or more presentations of the GUI are identified, and templates are associated with portions of the trace. Each template has one or more defined inputs and a defined output. For each template of the plurality of templates, the portions of the traces are processed to automatically configure the template by specifying a procedure for providing values of inputs to the template via the GUI and obtaining a value of an output. The automated dialog system is configured with the configured templates, thereby avoiding manual configuration of the dialog system.

An interaction assistant conducts multiple turn interaction dialogs with a user in which context is maintained between turns, and the system manages the dialog to achieve an inferred goal for the user. The system includes a linguistic interface to a user and a parser for processing linguistic events from the user. A dialog manager of the system is configured to receive alternative outputs from the parser, and selecting an action and causing the action to be performed based on the received alternative outputs. The system further includes a dialog state for an interaction with the user, and the alternative outputs represent alternative transitions from a current dialog state to a next dialog state. The system further includes a storage for a plurality of templates, and wherein each dialog state is defined in terms of an interrelationship of one or more instances of the templates.

Iteratively updating parameters for dialog states

Automated assistant for user interaction via speech

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Cupertino, CA, United States of America

Pengyu Chen