Alternative Input Representation Using Entity Expansion and Weighting
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Solution Overview
Problem
Natural language processing systems often misinterpret user inputs due to errors in speech recognition and natural language understanding, leading to undesired actions or unresponsive outputs, particularly when users refer to entities not known to the system.
Innovation Solution
Integrate query expansion and entity weighting techniques to establish an entity expansion knowledge base, linking entities from user inputs and system responses, and use entity weighting to determine alternative input representations that align with user intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If speech recognition and natural language understanding processing techniques are used to enable voice-based control, then human-computer interaction is improved, but misinterpretation of user inputs occurs leading to undesired actions
Solution Approach 1:
The system generates multiple alternative interpretations of the user's voice input, each with associated confidence scores. These alternatives are presented back to the user for confirmation or correction, allowing the system to learn from user feedback and improve future interpretations. This feedback mechanism resolves the contradiction by maintaining ease of voice-based control while improving reliability through iterative correction.
Solution Approach 2:
The system performs preliminary processing by generating multiple candidate interpretations before final execution. Instead of directly acting on the first recognized input, the system pre-processes the input by creating alternative representations and evaluating their confidence levels, allowing potential misinterpretations to be caught and corrected before undesired actions occur.
2Speed
If the system uses standard speech recognition techniques, then processing speed is maintained, but accuracy decreases when users refer to unknown entities
Solution Approach 1:
The system segments the entity recognition task into multiple independent pathways: exact matching, fuzzy matching, and alternative interpretation generation. Each pathway processes different aspects of entity recognition in parallel, allowing the system to maintain processing speed while improving accuracy for unknown entities through multiple comparison strategies.
Solution Approach 2:
The system creates a multi-functional entity recognition mechanism that handles both known and unknown entities through the same framework. The alternative interpretation generator serves multiple purposes: correcting speech recognition errors, handling unknown entities, and providing disambiguation, thereby improving precision without requiring separate specialized systems that would slow processing.
3Reliability
If the system generates alternative input representations, then user intent is better captured, but system complexity increases
Solution Approach 1:
The system introduces an intermediary component - the alternative interpretation generator - that sits between speech recognition and action execution. This intermediary creates multiple candidate meanings without requiring complete restructuring of the system, capturing user intent more reliably while containing complexity growth to a specific modular component rather than throughout the entire system.
4Measurement precision
If the system corrects misrecognized words, then response accuracy improves, but processing time increases
Solution Approach 1:
The system applies partial correction by generating multiple alternative interpretations rather than exhaustively searching for the single correct meaning. It selects a limited number of most plausible alternatives based on confidence scores and contextual relevance, achieving sufficient accuracy improvement without the time cost of complete exhaustive correction.
Data Source
AI summary
Techniques for determining alternative input representations using entity expansion and entity weighting are described. An entity expansion knowledge base is built by extracting entities from user input-system response pairs that resulted in satisfactory experiences. An extracted entity is associated with an initial score based on it being included in the user input only, in the system response only or both the user input and the system response. Entities co-occurring in the user input-system response pair are connected in the knowledge base. An overall score is associated with the connections based on the initial scores of the connected entities. Using the knowledge base, expansion entities related to an entity included in a user input are determined, and the expansion entities and user input entity are weighted. The weighting of the entities involves assigning a level to each entity based on pairs of user input-alternative input representation.