Dialog Management System for Seamless Skill Transition
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Solution Overview
Problem
Current natural language processing systems face challenges in seamlessly transitioning between skills during dialog sessions without explicit user intervention, leading to unsatisfactory user experiences due to misinterpretation of user inputs and errors in processing.
Innovation Solution
The system employs two stages of processing to determine user intents by retrieving potential intents from the active skill, other skills, and similar skills, and uses alternative representations of user inputs to generate appropriate responses, leveraging structured data and context to select the most relevant skill for each dialog turn.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system uses a single skill processing approach, then the device complexity is reduced, but the adaptability to handle diverse user intents across different skills deteriorates
Solution Approach 1:
The intent processing architecture is segmented into distinct modules: a first module retrieves potential intents from the active skill, a second module retrieves potential intents from other skills, and a third module retrieves potential intents from similar skills. This segmentation allows each module to specialize in specific skill contexts while working together to achieve comprehensive intent recognition across skill boundaries.
2Measurement precision
If the system retrieves intents from multiple skill sources, then the intent recognition accuracy is improved, but the processing time increases
Solution Approach 1:
The system performs preliminary actions by retrieving and storing potential intents from the active skill, other skills, and similar skills in advance of final intent determination. By pre-fetching candidate intents from multiple sources and preparing their associated data, the system reduces the computational burden during the final selection phase, thereby minimizing processing time while maintaining high accuracy.
3Measurement precision
If the system uses alternative representations of user inputs, then the interpretation accuracy is improved, but the processing complexity increases
Solution Approach 1:
The system introduces an intermediary layer that generates alternative representations of user inputs. This intermediary processing layer transforms the original input into multiple alternative forms, which are then evaluated against the retrieved intents from different skills. This intermediary step improves interpretation accuracy by considering multiple possible meanings without requiring the entire system to be redesigned for complex processing.
Data Source
AI summary
Techniques for determining an intent for a user input in a dialog are described. The system processes historic interaction data that is structured based skills and intents, with each skill-intent pair being associated with one or more past user inputs received by the system, one or more sample inputs, and one or more alternative representations of the user inputs. Based on processing of the historic interaction data and dialog data of previous turns of the dialog, the system determines potential intents for the user input of the current turn of the dialog. The potential intents may correspond to a presently active skill or another skill, enabling the user to interact with another skill during the dialog.