Natural Language Explanations for Transparent System Determinations
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Solution Overview
Problem
Natural language processing systems often face errors in speech recognition and understanding, leading to unsatisfactory user experiences due to incorrect interpretations of user inputs, and there is a need for improved transparency in system determinations to enhance user understanding and feedback mechanisms.
Innovation Solution
A system that generates natural language explanations for its determinations, using encoded representations of knowledge bases and contextual information to provide insights into its decision-making processes, allowing users to understand how outputs were generated and enabling feedback mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the system provides detailed explanations for its determinations, then user understanding and transparency improve, but system complexity and processing time increase
Solution Approach 1:
The explanation generation process is segmented into distinct components: identifying the determination type (ASR output, NLU output, or action), retrieving relevant knowledge base information, and formatting the explanation. This segmentation allows each component to be optimized independently while working together to provide comprehensive explanations without overwhelming complexity.
Solution Approach 2:
The system performs preliminary actions by pre-identifying the type of determination being made and pre-retrieving relevant knowledge base information before generating the final explanation. This preparation reduces the computational burden during explanation generation and ensures that only necessary information is processed, balancing transparency with complexity.
2Reliability
If the system provides explanations for all determinations, then user feedback mechanisms improve, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by selectively generating explanations based on the type of determination made. Not all determinations require full explanations, but the system ensures that explanations are provided where most needed (ASR outputs, NLU outputs, and actions) while avoiding unnecessary processing for routine operations. This approach balances feedback mechanism reliability with processing time efficiency.
Solution Approach 2:
The explanation generation process itself serves as a feedback mechanism, allowing users to understand system determinations and provide feedback that can improve future processing. The explanations enable users to identify and correct errors in ASR or NLU processing, creating a feedback loop that enhances system reliability without requiring excessive processing resources.
3Measurement precision
If the system uses encoded representations of knowledge bases, then determination accuracy improves, but data processing complexity increases
Solution Approach 1:
The system uses encoded representations of knowledge base information as an intermediary between the raw determination data and the final explanation. This intermediary layer translates complex determination data into a format that can be easily processed and presented to users, maintaining determination accuracy while reducing the complexity of data processing through standardized encoding and decoding mechanisms.
Data Source
AI summary
Techniques for generating and outputting a natural language explanation of a determination made by a system are described. The system presents content to a user, where the content is generated based on a system determination. The system determines history data associated with a user profile associated with the user and context data associated with the system determination. The system uses the history data and the context data to determine a natural language explanation that the output was generated based on the system determination. The system further uses the history data and the context data to generate a predicted system determination representing the system determination that resulted in the output presented to the user. Based on a similarity between the predicted system determination and the actual system determination, the natural language explanation is presented to the user.