Context-Enriched Attentive Memory Network for Dialogue Breakdown Detection
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Solution Overview
Problem
Current natural language processing systems for dialogue between users and electronic devices often fail to accurately detect and correct dialogue breakdowns, leading to user dissatisfaction due to incorrect responses, which can be caused by incomplete verbal inputs or misinterpretations.
Innovation Solution
The implementation of a context-enriched attentive memory network with global and local encoding to identify dialogue breakdowns by analyzing verbal inputs and previous responses, allowing for the generation of appropriate sound feedback when a breakdown is detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language processing is used to enable voice interaction, then ease of operation is improved, but reliability deteriorates due to dialogue breakdowns and incorrect responses
Solution Approach 1:
The system implements a dialogue breakdown detection mechanism that analyzes the relationship between user verbal inputs and system replies. When a breakdown is detected (indicating incorrect response), the system provides feedback by generating a follow-up question or comment to clarify the misunderstanding, thereby improving response accuracy while maintaining voice interaction ease
Solution Approach 2:
The patent introduces an intermediary detection layer between the natural language processing module and the response generation module. This intermediary analyzes contextual relationships and identifies dialogue breakdowns before final responses are generated, preventing incorrect responses while preserving the natural voice interaction flow
2Reliability
If contextual analysis is performed to detect dialogue breakdowns, then reliability is improved, but device complexity increases
Solution Approach 1:
The contextual analysis system is segmented into distinct functional modules: a verbal input processing module, a reply generation module, and a dialogue breakdown detection module. Each module handles specific aspects of context analysis, distributing the computational complexity across separate components rather than requiring a monolithic complex system
Solution Approach 2:
The system performs preliminary contextual encoding and analysis before generating responses. By pre-processing and storing contextual information from verbal inputs and interaction history, the system reduces the computational complexity required during real-time response generation while maintaining high detection accuracy
Data Source
AI summary
A method, an electronic device and computer readable medium for dialogue breakdown detection are provided. The method includes obtaining a verbal input from an audio sensor. The method also includes generating a reply to the verbal input. The method additionally includes identifying a local context from the verbal input and a global context from the verbal input, additional verbal inputs previously received by the audio sensor, and previous replies generated in response to the additional verbal inputs. The method further includes identifying a dialogue breakdown in response to determining that the reply does not correspond to the local context and the global context. In addition, the method includes generating sound corresponding to the reply through a speaker when the dialogue breakdown is not identified.