Joint Partial Dialog State and Entity Recognition Model
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Solution Overview
Problem
Dialog systems face uncertainty and reduced accuracy due to non-zero error rates in multiple processing stages, particularly in tracking the state of a conversation, which affects the robustness of the conversation state management.
Innovation Solution
A method that jointly models partial state update and named entity recognition using a sequence-based classification model, allowing for recognition and update of partial dialog states in a single processing stage at runtime, generating a distribution over partial dialog states with confidence scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple processing stages (ASR, LU, etc.) are used to process user input, then the system can perform comprehensive dialog state tracking, but the error rates accumulate and uncertainty increases
Solution Approach 1:
The patent combines multiple processing functions (ASR, LU, and dialog state tracking) into a single integrated processing stage. The neural network model simultaneously performs speech-to-text conversion, semantic understanding, and dialog state update in one unified operation, eliminating the sequential error accumulation that occurs when these functions are performed separately in multiple stages.
2Ease of manufacture
If traditional multi-stage processing is used, then each stage can be optimized independently, but the overall system robustness decreases due to error propagation
Solution Approach 1:
The patent merges ASR, LU, and dialog state tracking into a single neural network model that is trained end-to-end. This unified model processes user input through all functions simultaneously, allowing gradient flow and joint optimization across all tasks, which improves robustness by preventing error propagation between separate stages while maintaining training flexibility through the unified loss function.
3Measurement precision
If separate processing stages are used for state tracking and entity recognition, then each task can be specialized, but the processing complexity and uncertainty increase
Solution Approach 1:
The patent integrates entity recognition and dialog state tracking into a single processing framework using one neural network model. The model performs both entity recognition and state update operations simultaneously during a single forward pass, reducing processing complexity by eliminating multiple separate stages while maintaining specialized capabilities through shared representations and joint training.
Data Source
AI summary
Embodiments provide for tracking a partial dialog state as part of managing a dialog state space, but the embodiments are not so limited. A method of an embodiment jointly models partial state update and named entity recognition using a sequence-based classification or other model, wherein recognition of named entities and a partial state update can be performed in a single processing stage at runtime to generate a distribution over partial dialog states. A system of an embodiment is configured to generate a distribution over partial dialog states at runtime in part using a sequence classification decoding or other algorithm to generate one or more partial dialog state hypothesis and/or a confidence score or measure associated with each hypothesis. Other embodiments are included.