Multi-domain Dialog State Tracking via Question-Answer Model
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Solution Overview
Problem
Existing speech processing systems face challenges in effectively tracking and managing multi-domain dialog states, particularly in determining slot values across various domains within ongoing conversations, which affects the accuracy and coherence of user interactions.
Innovation Solution
The system employs a question-answer model that utilizes a knowledge graph to infer slot values by reading dialog context, using bidirectional attention layers and graph embeddings to predict slot values, even when the conversation spans multiple domains, and incorporates relationships between domains and slots to enhance performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional speech processing systems are used to track dialog states, then the system structure remains simple, but the accuracy of tracking multi-domain dialog states deteriorates
Solution Approach 1:
The system segments the dialog state tracking problem into multiple domain-specific components, where each domain (e.g., weather, news, sports) is handled by dedicated processing modules. This segmentation allows the system to maintain high accuracy for each domain while managing overall complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary dialog state tracker that mediates between user inputs and system responses across multiple domains. This intermediary component integrates information from various domains and maintains coherent dialog state, resolving the contradiction by adding a coordinating layer rather than fundamentally complicating the core processing.
2Adaptability or versatility
If the system tracks dialog states across multiple domains, then the versatility of the system improves, but the complexity of managing dialog states worsens
Solution Approach 1:
The dialog state tracker is designed with universal functionality that can handle multiple domains through a common framework. The system uses domain-agnostic mechanisms for state tracking, slot filling, and context management that can be applied across different domains, thereby achieving versatility without proportionally increasing complexity.
Solution Approach 2:
The system implements a nested structure where domain-specific dialog state trackers are embedded within a multi-domain framework. Each domain maintains its own state tracking mechanisms nested within the broader multi-domain context, allowing the system to scale to multiple domains while reusing common infrastructure.
3Measurement precision
If bidirectional attention layers and graph embeddings are used to infer slot values, then the precision of slot value prediction improves, but the computational complexity worsens
Solution Approach 1:
The system performs preliminary actions by pre-computing graph embeddings and attention weights during training phases. These pre-computed representations are stored and reused during inference, allowing the system to achieve high prediction precision without repeating computationally intensive calculations for every dialog turn.
Solution Approach 2:
The bidirectional attention mechanism applies partial computation by focusing attention only on relevant parts of the dialog context rather than processing all information equally. This selective attention approach achieves high precision in slot value prediction while reducing overall computational burden by ignoring irrelevant context.
Data Source
AI summary
A system for processing user utterances and/or text based queries that tracks context data of a current dialog between the system and the user, can determine values for slots by determining if the value was mentioned in a previous turn of the dialog. The system may employ a question-answer model and construct a question including the domain name, slot name, and potential slot values. The system may process the previous turns of the dialog and the constructed question to determine the slot value. The system may track a dialog that relates to multiple domains and may determine the slot value based on a slot value that is mentioned in relation to another domain.