Dialog Response Evaluation for Chatbot Engagement
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Solution Overview
Problem
Current speech processing systems face challenges in providing a desirable user experience through chatbot interactions, as generic responses often lead to bland conversations and low user satisfaction due to a lack of relevance and engagement in dialog exchanges.
Innovation Solution
A system is developed that evaluates potential responses for coherence and engagement using trained models, scoring them based on comprehensibility, topical relevance, interest, and the likelihood of continuing the conversation, selecting the most suitable responses to enhance dialog quality and user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generic responses are used in chatbot interactions, then system complexity is reduced and processing speed is improved, but user satisfaction deteriorates due to bland conversations and low engagement
Solution Approach 1:
The patent introduces dialog evaluators as intermediary components that assess potential responses before they are delivered to the user. These evaluators score responses based on coherence, engagement, and relevance metrics, acting as a mediator between the response generation system and the user interaction. This allows the system to maintain simple, fast response generation while filtering and selecting only high-quality responses that satisfy user engagement requirements.
Solution Approach 2:
The system implements feedback mechanisms where dialog evaluators continuously assess the quality of potential responses using trained models. The evaluators provide feedback scores that indicate whether a response maintains coherence with the conversation context and engages the user effectively. This feedback loop enables the system to adapt and select responses that improve user satisfaction without significantly increasing processing complexity.
2Reliability
If multiple trained models are used to evaluate and score potential responses, then response quality and user satisfaction are improved, but system complexity and computational resources increase
Solution Approach 1:
The patent segments the response evaluation process into multiple independent dialog evaluators, each specializing in specific aspects such as coherence, engagement, and relevance. Rather than using one complex monolithic model, the system divides the evaluation into modular components that can be trained and deployed separately. This segmentation reduces the complexity of individual models while maintaining overall system effectiveness through collective evaluation.
3Measurement precision
If dialog evaluators are trained on annotated training data, then response selection accuracy is improved, but training time and data processing requirements increase
Solution Approach 1:
The patent applies preliminary action by training dialog evaluators in advance using annotated training data before deployment. The evaluators are pre-trained on diverse conversation scenarios and response patterns, allowing them to quickly and accurately assess new responses during actual interactions. This preliminary training phase, while time-consuming, enables fast and accurate response selection during production use without requiring real-time training.
Data Source
AI summary
A system that can engage in a dialog with a user may select a system response to a user input based on how the system estimates a user may respond to a potential system response. Models may be trained to evaluate a potential system response in view of various available data including dialog history, entity data, etc. Each model may score the potential system response for various qualitative aspects such as whether the response is likely to be comprehensible, on-topic, interesting, likely to lead to the dialog continuing, etc. Such scores may be combined to other scores such as whether the potential response is coherent or engaging. The models may be trained using previous dialog/chatbot evaluation data. At runtime the scores may be used to select a system response to a user input as part of the dialog.