Context-Aware Dialog Grammar Selection
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Solution Overview
Problem
Existing automated assistants face difficulties in seamlessly transitioning between different domains of conversation, often requiring additional dialog or failing to respond appropriately when a user abruptly changes topics.
Innovation Solution
A contextual data structure is used to persist relevant topics during human-to-computer dialog, allowing the automated assistant to select appropriate grammars for parsing natural language input and generating responses, while dropping less relevant topics to reduce computational resources and prevent nonsensical outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If all grammars are applied to parse natural language input, then parsing coverage is improved, but computational resources and processing time increase
Solution Approach 1:
The grammar library is segmented into multiple categories (e.g., domain-specific grammars, general-purpose grammars, fallback grammars). The system selectively applies grammars based on the detected topic context, applying only relevant segments rather than all grammars uniformly. This segmentation allows comprehensive coverage when needed while maintaining fast processing by applying only necessary portions.
Solution Approach 2:
The grammar selection process is made dynamic through contextual filtering. The system dynamically determines which grammars to apply based on the current dialog context and detected topics. This dynamic approach allows the system to adapt grammar application to the specific situation, improving processing efficiency by avoiding unnecessary grammar applications while maintaining comprehensive coverage when context requires it.
2Measurement precision
If multiple grammars are applied to ensure comprehensive parsing, then parsing accuracy is improved, but response time increases
Solution Approach 1:
The system performs preliminary topic detection and contextual analysis before applying grammars. By identifying the current topic and relevant context in advance, the system can pre-filter the grammar set to only those likely to be relevant. This preliminary action ensures that when grammars are applied, they are already optimized for the specific context, maintaining high accuracy while reducing the number of grammars that need to be processed.
Solution Approach 2:
Different grammar sets are associated with different topics and contexts. The system applies high-quality, topic-specific grammars locally to relevant input portions rather than applying a single comprehensive grammar set globally. This local quality approach ensures parsing accuracy for each specific context while reducing overall processing time by avoiding unnecessary grammar applications.
3Adaptability or versatility
If a large number of grammars are maintained for various topics, then system versatility is improved, but device complexity increases
Solution Approach 1:
A context management intermediary layer is introduced between the grammar library and the parsing process. This intermediary maintains the contextual data structure, tracks active topics, and dynamically selects which grammars to apply. This mediator simplifies grammar management by abstracting the complexity of having numerous grammars, automatically handling selection based on context without requiring complex manual management.
Solution Approach 2:
The context data structure serves multiple functions: it tracks active topics, determines grammar applicability, manages dialog state, and coordinates between different parsing components. This universal context structure reduces overall system complexity by consolidating multiple management functions into a single multi-functional component, allowing the system to handle diverse topics without proportionally increasing complexity.
Data Source
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AI summary
Methods, apparatus, and computer readable media are described related to utilizing a context of an ongoing human-to-computer dialog to enhance the ability of an automated assistant to interpret and respond when a user abruptly transitions between different domains (subjects). In various implementations, natural language input may be received from a user during an ongoing human-to-computer dialog with an automated assistant. Grammar(s) may be selected to parse the natural language input. The selecting may be based on topic(s) stored as part of a contextual data structure associated with the ongoing human-to-computer dialog. The natural language input may be parsed based on the selected grammar(s) to generate parse(s). Based on the parse(s), a natural language response may be generated and output to the user using an output device. Any topic(s) raised by the parse(s) or the natural language response may be identified and added to the contextual data structure.