Dialogue System Human-in-the-Loop Deployment
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Solution Overview
Problem
Automated dialogue systems face a chicken-and-egg problem where they require a high level of performance to be useful, but achieving this performance necessitates data from real users, which is difficult to obtain due to generated or paid user data being less varied and of lower quality than real user data.
Innovation Solution
A dialogue system that rapidly deploys with a small amount of data by incorporating human assistance, using a machine learning model to generate ranked alternatives and a confidence model to decide when to seek human help, with human assistance provided by users or reviewers, allowing the system to improve over time as it collects more data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a dialogue system is deployed with minimal initial data, then deployment speed is improved, but system performance and reliability deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models offline with available data before deployment. The confidence model is trained in advance to predict when the system should seek human assistance, allowing the system to be deployed quickly with minimal initial data while maintaining reliability through pre-prepared decision-making capabilities.
Solution Approach 2:
A confidence model acts as an intermediary between the automated dialogue system and human reviewers. The confidence model assesses the reliability of system responses and determines when human assistance should be invoked, enabling the system to operate effectively with minimal initial data by selectively engaging human expertise only when necessary.
2Reliability
If human assistance is incorporated into the dialogue system, then system reliability is improved, but device complexity increases
Solution Approach 1:
The system is segmented into distinct functional components: the dialogue system, the confidence model, and the human reviewer interface. This segmentation allows each component to operate independently and efficiently, managing complexity by dividing the system into specialized modules that can be developed and maintained separately.
Solution Approach 2:
The confidence model serves as an intermediary layer that manages the interaction between the automated system and human reviewers. It automatically filters requests, presenting only those requiring human assistance, thereby simplifying the overall system architecture by automating the coordination and selection process rather than requiring direct complex integration between all components.
3Manufacturing precision
If the system seeks human assistance frequently, then response accuracy is improved, but processing time increases
Solution Approach 1:
The system dynamically changes the threshold parameter for invoking human assistance based on confidence levels. By adjusting this parameter, the system can optimize the balance between accuracy and response time - seeking human assistance only when confidence falls below the threshold, thereby maintaining high accuracy while minimizing unnecessary delays for high-confidence responses.
Solution Approach 2:
Human assistance is applied locally and selectively only to specific requests where the confidence model indicates uncertainty, rather than universally to all requests. This localized application of human review ensures high accuracy for problematic cases while maintaining fast automated processing for the majority of confident responses, optimizing the overall response time-accuracy tradeoff.
Data Source
AI summary
A method for a dialogue system includes establishing a dialogue session between an application executing on a server and a remote machine. The dialogue session includes one or more utterances received from a user at the remote machine. A natural language processing machine identifies a request associated with a computer-readable representation of an utterance. A dialogue expansion machine generates a plurality of alternative actions for responding to the request. A previously-trained machine learning confidence model assesses a confidence score for each alternative. If a highest confidence score for a top alternative does not satisfy a threshold, the plurality of alternatives including the top alternative are transmitted to a remote machine (which may be the same remote machine or a different remote machine) for review by a human reviewer. After the dialogue system and/or the human reviewer select an alternative, computer-readable instructions defining the selected alternative are executed.