Digital Assistant Intent Disambiguation With Common-Ancestor Modeling
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Solution Overview
Problem
Digital assistants often misinterpret user utterances, leading to unexpected outcomes or inefficient communication due to incorrect task execution.
Innovation Solution
A disambiguation system that utilizes a hierarchy model and natural language processing to determine predicted intents and slot values, generating a response that clarifies possible interpretations for user selection, thereby improving task execution accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a digital assistant uses natural language processing to interpret user utterances, then the assistant can process user requests, but it may incorrectly interpret the natural language leading to unexpected outcomes
Solution Approach 1:
The system provides feedback to the user by presenting multiple predicted intents with slot values and asking for confirmation or correction. This feedback loop allows the user to clarify their intent, ensuring accurate task execution while maintaining efficient processing speed.
Solution Approach 2:
The system performs preliminary action by predicting multiple possible intents before executing any task. It presents these predicted intents to the user for confirmation, preventing incorrect interpretation and ensuring accuracy before the actual task execution occurs.
2Reliability
If the digital assistant provides multiple possible interpretations for user utterances, then intent accuracy improves, but communication efficiency decreases
Solution Approach 1:
The system applies partial action by providing only the top predicted intents with slot values rather than all possible interpretations. This selective presentation maintains high accuracy while minimizing the time required for user confirmation and system response.
3Measurement precision
If the system uses a hierarchy model to determine common ancestors of predicted intents, then disambiguation accuracy improves, but system complexity increases
Solution Approach 1:
The hierarchy model serves multiple functions: it organizes intents by category, determines common ancestors for disambiguation, and structures the response presentation. This multi-functionality achieves high disambiguation precision while managing system complexity through a unified data structure.
Data Source
AI summary
A computer-implemented method includes receiving an utterance from a user comprising an intent, the intent defining a task executable by the digital assistant in response to the utterance. The method also includes transmitting the utterance to a natural language processing system. The method further includes receiving predicted intents and slot values determined by the natural language processing system based on the utterance, the predicted intents defining tasks executable by the digital assistant and the slot values providing information usable by the digital assistant to execute the tasks. Additionally, the method includes determining, based on a hierarchy model, a common ancestor for the predicted intents. Further, the method includes generating a response for display to the user using the predicted intents, the slot values, and the common ancestor, the response usable by the digital assistant to determine the intent.