Dialog Device Template Generation for Character-Like Interaction
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Solution Overview
Problem
Existing dialog systems lack character-ness, especially when there is a small amount of question response data, limiting their ability to perform interactions beyond simple question-and-answer exchanges and failing to provide context-based responses.
Innovation Solution
A dialog device that collects question response data associated with states or dialog acts, generates utterance templates, and uses these templates to produce system utterances, allowing for character-like interactions even with limited data, by incorporating a question response collection unit, template generation unit, utterance generation unit, and state transition unit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a dialog system uses a small amount of question response data, then the system can be constructed quickly with minimal data collection effort, but the response capability and interaction quality become low
Solution Approach 1:
The patent segments the dialog system construction into two phases: (1) collecting a small amount of high-quality question-response pairs with explicit state/dialog act annotations, and (2) using these segmented data points to train a deep learning model that learns contextual patterns. This segmentation allows quick construction while maintaining reliability through targeted data quality over quantity.
Solution Approach 2:
The patent performs preliminary action by pre-annotating a small set of question-response pairs with state information or dialog act labels before training the deep learning model. This preliminary labeling of key examples enables the model to learn contextual patterns efficiently, achieving high response capability without requiring extensive labeled data.
2Adaptability or versatility
If a dialog system is designed to perform context-based interactions beyond simple Q&A, then the interaction quality improves, but the system complexity and data requirements increase significantly
Solution Approach 1:
The patent introduces state information or dialog act labels as an intermediary between user input and system response. This intermediary layer enables context-based interactions by capturing the functional meaning of dialog turns, allowing the system to perform sophisticated interactions without directly increasing the complexity of the utterance generation component.
Solution Approach 2:
The patent replaces complex rule-based dialog management mechanisms with a deep learning model that learns contextual patterns from data. This substitution reduces explicit system complexity by using statistical learning instead of hand-crafted rules, while maintaining high adaptability for context-based interactions.
3Reliability
If extensive question response data is collected to improve interaction quality, then the response capability increases, but the data collection effort and time consumption increase
Solution Approach 1:
The patent changes the parameter of data quality by focusing on collecting a small amount of highly annotated data with explicit state or dialog act labels, rather than collecting large amounts of unannotated or lightly annotated data. This parameter change allows the deep learning model to achieve high response capability with minimal data collection time.
4Stability of the object's composition
If the utterance generation unit is reconfigured to impart character-ness, then the character consistency improves, but the system requires extensive reconfiguration effort
Solution Approach 1:
The patent changes the approach to character consistency by incorporating character profile information as input parameters to the deep learning model during training, rather than reconfiguring the utterance generation unit's structural parameters. This allows character consistency to be achieved through data-driven learning while minimizing reconfiguration effort.
Data Source
AI summary
Even when there is a small amount of question response data, a highly accurate response is performed to a user utterance. A question response collection unit (12) collects question response data including a state of a dialog, a question, and a response. A template generation unit (13) generates an utterance template associated with the state on the basis of the question response data. An utterance generation unit (14) generates a system utterance by using the utterance template associated with a state of a current dialog. An utterance presentation unit (15) presents the system utterance to a user. An utterance reception unit (16) receives a user utterance uttered by the user. A state transition unit (17) causes the state of the current dialog to transition on the basis of the user utterance.