Microsoft Technology Licensing, LLC

Semantic Machines, Inc.

Jacob Daniel Andreas

David Leo Wright Hall

Daniel Louis Klein

Jesse Daniel Eskes Rusak

Adam David Pauls

Jayant Sivarama Krishnamurthy

Laurence Steven Gillick

John P Bufe, Iii

Pengyu Chen

Alan Xinyu Guo

Daniel Lawrence Roth

Michael Jack Newman

Jordan Rian Cohen

Andrew Volpe

David Ernesto Heekin Burkett

Jason Andrew Wolfe

Yuchen Zhang

Steven Andrew Wegmann

Taylor Darwin Berg-Kirkpatrick

Andrei Vorobev

Alexander J Kolmykov-Zotov

Benjamin David Van Durme

Jason Michael Eisner

Samuel McIntire Thomson

Hao Fang

Eui Chul Shin

Harsh Jhamtani

Patrick Aozhe Xia

Kaj Alexander Nelson Bostrom

Implementations of semantic parsing using pre-trained language models are provided. One aspect includes a computing system for semantic parsing of natural language. The computing system comprises processing circuitry and memory containing instructions that, when executed, cause the processing circuitry to receive a request comprising a natural language utterance and generate a formal meaning representation using the natural language utterance and a language model comprising a semantic parser that has been prompted with training data generated by providing a dataset comprising a set of unlabeled programmatic scripts and a seed programmatic script, generating a set of parsed natural language descriptions by inputting the set of unlabeled programmatic scripts into an inverse semantic parser, generating a set of re-parsed programmatic scripts by inputting the set of parsed natural language descriptions into the semantic parser, and determining a set of labeled programmatic scripts by validating the set of re-parsed programmatic scripts.

Semantic parsing with pre-trained language models

A computer-implemented method of responding to a conversational event is presented. The method comprises receiving a conversational event at a conversational computing interface. Based on the received conversational event, an applicable generation rule of a plurality of candidate generation rules is selected. The applicable generation rule is configured with one or more parameters. A computer-executable plan is then selected based on the selected generation rule. The one or more parameters are passed from the selected generation rule to one or more additional generation rules. The one or more additional generation rules configured with the one or more parameters are recursively applied to extend the selected computer-executable plan. One or more candidate responses to the conversational event are output via the conversational computing interface based on the recursive application of the one or more additional generation rules configured with the one or more parameters.

Response generation for conversational computing interface

A computer-implemented method of responding to a conversational event. The method comprises enacting, by a conversational computing interface, an initial computer-executable plan based on a conversational event received by the conversational computing interface, wherein the initial computer-executable plan is configured to output an initial value based on the conversational event. The method further comprises selecting, by the conversational computing interface, an extended computer-executable plan based on determining that the initial value is insufficient for generating an extended description responsive to the conversational event. The method further comprises enacting, by the conversational computing interface, the extended computer-executable plan to output additional information beyond what the initial computer-executable plan is configured to output, the additional information sufficient for generating the extended description responsive to the conversational event. The method further comprises outputting, via the conversational computing interface, a response to the conversational event including the extended description.

A method comprising recognizing a user utterance including an ambiguity. The method further comprises using a previously-trained code-generation machine to produce, from the user utterance, a data-flow program including a search-history function. The search-history function is configured to select a highest-confidence disambiguating concept from one or more candidate concepts stored in a context-specific dialogue history.

Ambiguity resolution with dialogue search history

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, comprising recognizing a user utterance for processing. The method further comprises using a previously-trained code-generation machine to generate, from the user utterance, a data-flow program configured to produce a return value upon successful execution. The method further comprises beginning execution of the data-flow program. Responsive to reaching an error condition resulting from execution of the data-flow program, the method further comprises, prior to the data-flow program producing the return value, suspending execution of the data flow program. The method further comprises using the previously-trained code-generation machine to generate an error-handling data-flow program, wherein the error-handling data-flow program is configured to produce the return value; beginning execution of the error-handling data-flow program to produce the return value; and outputting the return value.

Error recovery for conversational systems

A system that generates natural language content. The system generates and maintains a dialogue state representation having a process view, query view, and data view. The three-view dialogue state representation is continuously updated during discourse between an agent and a user, and rules can be automatically generated based on the discourse. Upon a content generation event, an object description can be generated based on the dialogue state representation. A string is then determined from the object description, using a hybrid approach of the automatically generated rules and other rules learned from annotation and other user input. The string is translated to text or speech and output by the agent. The present system also incorporates learning techniques, for example when ranking output and processing annotation templates.

Natural language content generator

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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San Francisco, CA, United States of America

Jacob Daniel Andreas