Multi-Intent Speech Parsing for Colloquial Query Understanding
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Solution Overview
Problem
Existing automated chat systems struggle to accurately interpret natural language queries, particularly in complex or colloquial speech, due to variations in sentence structure and word order, limiting their conversational capabilities.
Innovation Solution
A network-based system that utilizes a speech analysis computer device to parse natural language speech by labeling words, detecting potential splits, and dividing statements into multiple intents, enabling the generation of responses based on these intents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional chat systems use simple command understanding, then system complexity is reduced, but the ability to understand natural language speech deteriorates
Solution Approach 1:
The system segments natural language speech into multiple intents by detecting potential splits in the verbal statement. Each split divides the speech into separate intent components that can be processed independently, allowing the system to handle complex natural language queries without requiring complete reprocessing of the entire statement.
Solution Approach 2:
The system adds a new dimension to speech processing by implementing multi-layered analysis including word structure analysis, label-based detection, and distance-based reduction. This multi-dimensional approach enables the system to understand natural language speech comprehensively while maintaining organized processing through distinct analytical layers.
2Measurement precision
If chat systems parse speech into multiple intents, then understanding accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by detecting potential splits and analyzing word structures before final intent division. By identifying and marking potential split points in advance, the system prepares the speech data for efficient intent separation, reducing the computational burden during the actual processing phase.
Solution Approach 2:
The system uses the inherent structure of the speech itself to guide the parsing process. By analyzing word structures, labels, and distances within the verbal statement, the system automatically identifies meaningful split points without requiring external guidance or manual intervention, making the processing efficient and adaptive to different speech patterns.
3Manufacturing precision
If the system analyzes word structure and labels to detect splits, then intent division accuracy is improved, but computational complexity increases
Solution Approach 1:
The system applies partial analysis by focusing on specific linguistic features (word structure, labels, distances) rather than analyzing every aspect of the speech. This selective approach achieves sufficient intent division accuracy without requiring complete linguistic analysis, balancing precision with computational efficiency.
Solution Approach 2:
The system introduces labels as intermediary elements that bridge the gap between raw speech and intent division. These labels serve as intermediate representations that capture essential linguistic features, making the complex task of intent division more manageable by providing structured intermediate data that guides the splitting process.
Data Source
AI summary
A system for parsing separate intents in natural language speech configured to (i) receive, from the user computer device, a verbal statement of the user including a plurality of words; (ii) translate the verbal statement into text; (iii) label each of the plurality of words in the verbal statement; (iv) detect one or more potential splits in the verbal statement; (v) divide the verbal statement into a plurality of intents based upon the one or more potential splits; and (vi) generate a response based upon the plurality of intents.