Microsoft Technology Licensing, LLC

Puyang Xu

Ruhi Sarikaya

Alexandre Rochette

Asli Celikyilmaz

Christopher A Meek

Daniel Boies

Zhaleh Feizollahi

Asela Jeevaka Ranaweera Gunawardana

Methods and systems are provided for contextual language understanding. A natural language expression may be received at a single-turn model and a multi-turn model for determining an intent of a user. For example, the single-turn model may determine a first prediction of at least one of a domain classification, intent classification, and slot type of the natural language expression. The multi-turn model may determine a second prediction of at least one of a domain classification, intent classification, and slot type of the natural language expression. The first prediction and the second prediction may be combined to produce a final prediction relative to the intent of the natural language expression. An action may be performed based on the final prediction of the natural language expression.

Contextual language understanding for multi-turn language tasks

Systems and methods for determining a user intent or goal for contextual language understanding by utilizing information from one or more previous user natural language inputs and one or more previous system generated responses to the user natural language inputs are provided. More specifically, the systems and methods utilize a common schema for determining features from the responses and natural language inputs and provide carryover tracking between responses and the natural language inputs. Accordingly, the systems and methods for contextual language understanding provide for a more accurate, a more reliable, and a more efficient context carryover and goal tracking system when compared to systems and methods that do not utilized the responses in determining the user goal/intent.

Context carryover in language understanding systems or methods

A method of generating a decision graph from event stream data, wherein the event stream data includes a plurality of events, and each event includes an associated time stamp, includes generating decision nodes for the graph, wherein the decision nodes each comprise a question having a temporal element. The method includes generating leaf nodes for the graph, wherein the leaf nodes each comprise a rate parameter, and iteratively splitting and merging nodes in the graph in order to maximize a measure of purity of outcomes in resulting nodes.

Using event stream data to create a decision graph representing a race participant where leaf nodes comprise rates defining a speed of the race participant in a race simulation

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Puyang Xu