Spoken Language Understanding Abnormality Detection
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Solution Overview
Problem
In spoken language understanding systems, analysis abnormalities often go undetected, leading to suboptimal user experience and inefficient dialogue processing, as existing methods only output abnormality information when explicit errors occur, lacking comprehensive detection.
Innovation Solution
A method and apparatus that acquire and analyze spoken language information and its corresponding analysis results to determine the presence of intent and predefined word slot values, outputting abnormality information when these are missing or mismatched, and utilizing similarity thresholds to identify intent changes, thereby enhancing the detection of analysis abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If abnormality information is only output when explicit errors occur, then the system maintains simple detection logic, but analysis abnormalities go undetected leading to suboptimal user experience
Solution Approach 1:
The system performs preliminary analysis by checking whether spoken language information contains predefined word slot values before final processing. This early detection mechanism identifies potential abnormalities in intent recognition and word slot extraction, allowing the system to flag issues before they propagate through the dialogue system, thereby improving detection comprehensiveness without substantially increasing complexity
Solution Approach 2:
The system implements feedback by comparing the presence of word slot values in the original spoken language information against the analysis results. When mismatches or abnormalities are detected, the system outputs abnormality information that feeds back into the dialogue management process, enabling corrective actions and improving overall system reliability
2Reliability
If comprehensive detection of analysis abnormalities is implemented, then user experience and system performance are enhanced, but the detection process becomes more complex and resource-intensive
Solution Approach 1:
The detection process is segmented into distinct components: intent recognition analysis, word slot extraction analysis, and abnormality determination. Each component independently evaluates specific aspects of the spoken language understanding, allowing comprehensive detection through modular analysis rather than a monolithic complex process
Solution Approach 2:
The system applies different detection strategies to different parts of the analysis process. Specifically, it checks for the presence of predefined word slot values in the spoken language information and compares this with the extracted word slots, applying localized quality checks to specific critical points in the analysis pipeline rather than uniformly complex detection throughout
3Measurement precision
If the system checks for predefined word slot values and intent information, then analysis abnormalities are identified more accurately, but processing time increases
Solution Approach 1:
The system performs preliminary checks on the spoken language information to identify the presence of predefined word slot values before the main analysis processing. This early identification allows for faster abnormality detection by pre-marking key elements that will be compared against analysis results, reducing the time required for comprehensive precision checking
Solution Approach 2:
The system extracts and isolates specific critical elements (predefined word slot values and intent information) from the full spoken language input for focused comparison against analysis results. By extracting only the essential elements needed for abnormality detection rather than analyzing the entire input comprehensively, the system achieves high detection precision with reduced processing time
Data Source
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AI summary
Embodiments of the present disclosure provide a method and apparatus for outputting analysis abnormality information in a spoken language understanding. A specific embodiment of the method includes: acquiring first spoken language information and a first analysis result for the first spoken language information; determining whether the first analysis result includes intent information; determining whether the first spoken language information includes a predefined word slot value, in response to determining the first analysis result not including the intent information; and outputting information indicating an existence of an abnormality in the first analysis result, in response to determining the first spoken language information including the predefined word slot value. The embodiment improves the efficiency of outputting the analysis abnormality information in the spoken language understanding.