Dialog Policy Acquisition via K-Segment Simulation
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Solution Overview
Problem
The existing Multi-Agent Task-Oriented Dialog Policy Learning (MADPL) methods face challenges in generalizing effectively to real-world scenarios due to limited action combinations in artificial dialog datasets, leading to poor performance in unseen human-computer dialog contexts.
Innovation Solution
The method involves acquiring a dialog policy by first characterizing the current dialog state, obtaining a hidden vector, and then simulating K single-action dialogs based on this vector to model dialog state transitions in a compact hidden state space, thereby improving planning efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If MADPL directly imitates action combinations in artificial dialog dataset, then learning process is simple, but generalization effect in real-world scenario is poor
Solution Approach 1:
The patent performs preliminary simulation of K single-action dialogs before obtaining the dialog policy. By simulating multiple possible action sequences in advance based on the hidden vector, the system prepares diverse action combinations that can generalize to real-world scenarios, resolving the contradiction between simple learning and good generalization.
Solution Approach 2:
The patent creates a simplified copy of the dialog state transition process by simulating K single-action dialogs based on the hidden vector. This simulation copies the essential dynamics of real dialog transitions in a compact hidden state space, enabling the model to learn generalization patterns without requiring complex real-world environmental modeling.
2Ease of manufacture
If artificial dialog dataset is used for training, then data collection is easy, but action combinations are limited and coverage is insufficient
Solution Approach 1:
The system uses itself to generate additional training data by simulating K single-action dialogs based on the hidden vector derived from dialog state characterization. This self-service approach allows the model to expand its action combination coverage internally without requiring external data collection, maintaining ease of data acquisition while significantly increasing action variety.
Solution Approach 2:
The patent transitions from two-dimensional artificial dialog datasets to a multi-dimensional simulation space by generating K different single-action dialog sequences from a single hidden vector. This dimensional expansion enables the system to explore diverse action combinations that would be difficult to capture in traditional datasets, effectively increasing action coverage without proportionally increasing data collection complexity.
3Productivity
If K single-action dialogs are simulated based on hidden vector, then planning efficiency and accuracy improve, but computational complexity increases
Solution Approach 1:
The patent segments the dialog planning process into K independent single-action dialog simulations, each processed separately based on the hidden vector. By dividing the complex multi-action planning into multiple simpler single-action simulations, the system improves planning efficiency through parallel processing while keeping each individual computation relatively simple, thus managing overall computational complexity.
Data Source
AI summary
A dialog strategy obtaining method and apparatus and a related device. The method comprises: obtaining first information used for representing a current dialog state (101); on the basis of the first information, obtaining a first hidden vector corresponding to the current dialog state (102); and then according to the first hidden vector, simulating a K-segment single-action dialog to obtain a dialog strategy (103).