Multi-way Classifier Dialogue System for Flexible Intent Recognition
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Solution Overview
Problem
Current dialogue systems face challenges in efficiently processing complex user inputs and generating accurate responses, particularly in goal-oriented dialogues, due to resource utilization inefficiencies and the need for pre-specified conversational pathways.
Innovation Solution
A dialogue system employing multi-way classifier models for each dialogue slot, which process input signals to recognize relationships between user expressions and slots, and a policy model that determines responses based on a belief state and world state, reducing memory requirements and enabling flexible conversational pathways.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If pre-specified conversational pathways are used to structure dialogue, then dialogue flow is controlled and predictable, but system flexibility and adaptability to complex user inputs deteriorate
Solution Approach 1:
The patent segments the dialogue system into distinct modular components: classifier models for intent recognition, belief state trackers for context management, and policy models for response generation. This segmentation allows each component to specialize in specific functions while maintaining overall system flexibility and control.
Solution Approach 2:
The patent implements dynamic belief states that evolve during dialogue based on user inputs, rather than following rigid pre-specified pathways. The system adapts its internal state representation dynamically, allowing flexible response to complex or unexpected user inputs while maintaining coherent dialogue flow.
2Measurement precision
If comprehensive training data is collected to improve dialogue system accuracy, then model performance improves, but data collection time and resource utilization increase
Solution Approach 1:
The patent performs preliminary generation of synthetic training data through simulated dialogues before actual system deployment. This preliminary action creates a foundational training set that can be refined with smaller amounts of real user data, significantly reducing the time and resources needed for comprehensive data collection while maintaining high accuracy.
Solution Approach 2:
The patent uses synthetic copying of dialogue scenarios through simulation to create training data. By generating artificial but realistic dialogue examples through virtual agents and scripted interactions, the system obtains diverse training samples without requiring equivalent real-world data collection efforts.
3Measurement precision
If multiple classifier models are used for each dialogue slot to improve intent recognition, then accuracy improves, but computational complexity and resource requirements increase
Solution Approach 1:
The patent divides the classification task into multiple specialized classifier models, each dedicated to specific dialogue slots or intent categories. This segmentation allows each classifier to be optimized for its specific function with fewer parameters, reducing overall computational complexity while maintaining or improving recognition accuracy through specialized processing.
Solution Approach 2:
The patent designs classifier models with shared underlying architectures and parameters that can be applied across multiple dialogue slots and intent types. This universal approach allows the system to use multiple classifiers without proportionally increasing complexity, as the models share common computational components and can be trained efficiently using transfer learning.
Data Source
AI summary
A dialogue system comprising: an input for receiving input data relating to a speech or text signal originating from a user; an output for outputting speech or text information specified by a dialogue act; and a processor configured to: generate features from the input signal; for each of a plurality of classifier models, each classifier model corresponding to a dialogue slot, and for one or more values corresponding to the dialogue slot, input features generated from the input signal, the classifier model outputting a probability corresponding to each of three or more relations, wherein the relations specify the relation of the value to the dialogue slot; update a belief state based on the outputs of the classifier models; determine a system dialogue act by inputting information relating to the belief state into a policy model; output speech or text information specified by the determined dialogue act at the output.