Pseudo-Dialogue Data Generation for Dialogue System Verification
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Solution Overview
Problem
Existing dialogue systems that automatically generate scenarios dynamically or lack a predefined dialogue scenario pose challenges in verifying their operation, as traditional methods for analyzing logs cannot effectively assess their performance.
Innovation Solution
An information processing apparatus that generates pseudo-dialogue data by extracting keywords from a FAQ collection, conducting simulated dialogues, and aggregating data on keyword usage frequencies to provide insights into dialogue system operation and user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a dialogue system automatically generates scenarios dynamically or does not use a predefined dialogue scenario, then the system's flexibility and adaptability improve, but the ability to verify operation through traditional log analysis deteriorates
Solution Approach 1:
The patent creates pseudo-dialogue data that copies the structure and characteristics of real user dialogues. This synthetic data replicates the dynamic scenario generation process, allowing verification without needing actual user interactions. The pseudo-dialogue maintains the same statistical properties and flow patterns as real dialogues while being fully controllable and reproducible.
Solution Approach 2:
The system performs preliminary generation of pseudo-dialogue data before actual operation verification. By pre-generating synthetic dialogue scenarios that cover various possible user interactions, the system prepares verification data in advance, enabling comprehensive operation checking without waiting for real user inputs.
2Ease of manufacture
If traditional log analysis methods are used to verify dialogue system operation, then the analysis process is simple, but the verification is ineffective for dynamically generated scenarios
Solution Approach 1:
The patent introduces pseudo-dialogue data as an intermediary between the dialogue system and verification processes. This intermediate synthetic data serves as a bridge that allows traditional analysis methods to effectively verify dynamic scenarios. The pseudo-dialogue acts as a controllable proxy that preserves the essential characteristics needed for verification while eliminating the limitations of analyzing only real user logs.
3Ease of operation
If a FAQ collection is used to guide user questions, then the dialogue system's responsiveness improves, but the complexity of managing and optimizing the FAQ collection increases
Solution Approach 1:
The patent implements feedback mechanisms where pseudo-dialogue analysis provides information about FAQ collection performance. By analyzing patterns in synthetic dialogue data, the system identifies which FAQ entries are most frequently accessed, which keywords are most important, and where optimizations are needed. This feedback loop enables data-driven optimization of the FAQ collection, reducing management complexity through automated insights.
Solution Approach 2:
The system uses parameter analysis of pseudo-dialogue data to optimize FAQ collection characteristics. By examining keyword frequencies, dialogue flow patterns, and user interaction metrics in the synthetic data, the system can adjust FAQ entry parameters such as keyword selection, question phrasing, and answer structure to improve overall system responsiveness.
Data Source
AI summary
According to one embodiment, an information processing apparatus includes a processing circuit. The processing circuit generates each of keywords stored in a frequently asked question (FAQ) collection as an utterance sentence. The processing circuit generates dialogue data for each of the keywords, the dialogue data generated by performing a dialogue for each of the keywords at least once, the dialogue obtained by generating a response sentence to the utterance sentence based on a result of searching the FAQ collection by use of the utterance sentence. The processing circuit generates aggregation data representing how often each of the keywords is used in the dialogue, based on the dialogue data.