Spoken Language Understanding Apparatus for Intent Recognition
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Solution Overview
Problem
Existing dialog systems struggle to accurately recognize the intention behind natural, free-form user utterances due to the vast variations in spoken language, limiting their ability to prepare probability tables for conditional probabilities of user intentions.
Innovation Solution
A spoken language understanding apparatus that includes a storage unit for pre-defined situation and intention information, an acquisition unit for natural sentences and situation data, and an analyzer that generates semantic representations to estimate user intentions using Bayes' theorem, allowing for the recognition of user intentions in real-time dialog systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-defined probability tables for conditional probabilities of user intentions are prepared, then the system can efficiently recognize user intentions, but the system cannot handle the vast variations in natural, free-form user utterances
Solution Approach 1:
The patent introduces a semantic representation as an intermediary between the user's natural utterance and the intention recognition system. The semantic representation unit converts diverse natural sentences into standardized semantic structures that can be matched against pre-defined probability tables, thereby bridging the gap between natural language variability and structured intention recognition
Solution Approach 2:
The system transforms the variable parameter of natural sentence formulation into a standardized semantic representation format. By changing the parameter from surface-level sentence structure to underlying semantic meaning, the system maintains consistent intention recognition across diverse utterances while preserving adaptability to natural language variations
2Ease of manufacture
If the system limits intentions to pre-defined categories, then probability tables can be prepared, but the system cannot recognize user intentions that fall outside predefined categories
Solution Approach 1:
The patent implements a dynamic intention recognition mechanism that combines pre-defined probability tables with real-time semantic analysis. The system adaptively selects between using pre-defined intentions and estimating new intentions based on semantic representations, allowing it to maintain implementation simplicity while expanding intention coverage dynamically
Solution Approach 2:
The system performs preliminary semantic representation of user utterances before intention recognition. This preliminary action enables the system to handle both pre-defined and new intentions by first converting natural language to semantic structures, then determining whether to match against pre-defined categories or estimate new intentions
Data Source
AI summary
According to one embodiment, an apparatus includes a storage unit, a first acquisition unit, a second acquisition unit, an analyzer, and a recognition unit. The storage unit stores first situation information about a situation assumed in advance, a first representation representing a meaning of a sentence assumed, intention information representing an intention to be estimated, and a first value representing a degree of application of the first representation to the first situation information and the intention information. The first acquisition unit acquires a natural sentence. The second acquisition unit acquires second situation information about a situation when acquiring the natural sentence. The analyzer analyzes the natural sentence and generates a second representation representing a meaning of the natural sentence. The recognition unit obtains an estimated value based on the first value associated with the first situation information and the first representation.