Amazon Technologies, Inc.

Melanie Chie Bomke Gens

Kellen Gillespie

Spyridon Matsoukas

Rahul Goel

Alexandra R Shapiro

Rahul Gupta

Chengwei Su

Shirin Saleem

Sankaranarayanan Ananthakrishnan

Kelly James Vanee

Kavya Ravikumar

John Will Crimmins

John Pelak

Methods and systems for determining an intent of an utterance using contextual information associated with a requesting device are described herein. Voice activated electronic devices may, in some embodiments, be capable of displaying content using a display screen. Entity data representing the content rendered by the display screen may describe entities having similar attributes as an identified intent from natural language understanding processing. Natural language understanding processing may attempt to resolve one or more declared slots for a particular intent and may generate an initial list of intent hypotheses ranked to indicate which are most likely to correspond to the utterance. The entity data may be compared with the declared slots for the intent hypotheses, and the list of intent hypothesis may be re-ranked to account for matching slots from the contextual metadata. The top ranked intent hypothesis after re-ranking may then be selected as the utterance's intent.

Intent re-ranker

Methods and systems for resolving entities using multi-modal functionality are described herein. Voice activated electronic devices may, in some embodiments, be capable of displaying content using a display screen. Contextual metadata representing the content rendered by the display screen may describe entities having similar attributes as an identified intent from natural language understanding processing. When natural language understanding processing attempts to resolve one or more declared slots for a particular intent, matching slots from the contextual metadata may be determined, and the matching entities may be placed in an intent selected context file to be included with the natural language understanding's output data. The output data may be provided to a corresponding application for causing one or more actions to be performed.

Contextual entity resolution

A natural language understanding system that can determine an overall score for a natural language hypothesis using hypothesis-specific component scores from different aspects of NLU processing as well as context data describing the context surrounding the utterance corresponding to the natural language hypotheses. The individual component scores may be input into a feature vector at a location corresponding to a type of a device captured by the utterance. Other locations in the feature vector corresponding to other device types may be populated with zero values. The feature vector may also be populated with other values represent other context data. The feature vector may then be multiplied by a weight vector comprising trained weights corresponding to the feature vector positions to determine a new overall score for each hypothesis, where the overall score incorporates the impact of the context data. Natural language hypotheses can be ranked using their respective new overall scores.

Contextual natural language processing

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Seattle, WA, United States of America

Melanie Chie Bomke Gens