Adaptive Dialogue Strategy Using Information Gain
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Solution Overview
Problem
Existing dialogue systems using close-ended questions can lead to lengthy and undesirable interactions when dealing with a large number of documents or items, as they fail to efficiently manage the conversation flow.
Innovation Solution
Implementing an adaptive dialogue strategy framework that incorporates open-ended questions at optimal times, using an information gain component to estimate the change in entropy and select between close-ended and open-ended questions based on expected information gain, thereby optimizing the dialogue strategy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If close-ended questions are used in dialogue systems, then the system can obtain specific information, but the dialogue length increases and interaction efficiency decreases
Solution Approach 1:
The patent implements dynamic question type selection that adapts between close-ended and open-ended questions based on real-time dialogue state and information needs. The system transitions from static question templates to dynamic question generation, adjusting question characteristics according to the current dialogue context and remaining information requirements.
Solution Approach 2:
The patent changes the parameter of question structure from fixed close-ended format to variable formats including open-ended questions. By modifying question parameters such as specificity, constraint level, and expected answer type, the system optimizes information gathering efficiency while reducing unnecessary dialogue turns.
2Loss of time
If open-ended questions are used to reduce dialogue length, then interaction efficiency improves, but information precision may decrease
Solution Approach 1:
The patent incorporates feedback mechanisms where the system evaluates user responses to open-ended questions and determines whether additional close-ended questions are needed to clarify or obtain specific details. This feedback loop ensures that information precision is maintained by selectively applying close-ended questions when precision is required while using open-ended questions for broader information gathering.
Solution Approach 2:
The patent segments the information gathering process into multiple stages: initial open-ended questions for broad information collection, followed by targeted close-ended questions for specific details. This segmentation allows the system to efficiently obtain both general context and precise information without requiring all questions to be of the same type.
3Productivity
If adaptive question selection is implemented, then dialogue efficiency improves, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary question selection module that acts as a mediator between the dialogue state and question generation components. This intermediary component analyzes the current dialogue context and selects appropriate question types, simplifying the overall system architecture by centralizing the decision-making logic rather than distributing complexity across multiple components.
Data Source
AI summary
Techniques that facilitate generating and executing an optimal dialogue strategy are provided. In one example, a system includes an information gain component and a question selector component. The information gain component estimates information gain data associated with first dialogue data of an information system. The information gain data is indicative of an amount of change in entropy associated with the first dialogue data. The question selector component selects between second dialogue data associated with a first question strategy and third dialogue data associated with a second question strategy based on the information gain data.