Chatbot Training Parameter Determination via Human-Human Interaction Analysis
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Solution Overview
Problem
Current methods for training chatbots to engage in human-like conversations are data-intensive and time-consuming, often mimicking human-machine interactions rather than human-human dialogues, which limits their ability to provide natural and efficient communication.
Innovation Solution
A system and method that analyze human-human interactions to determine trainable parameters, which are then used to update a training algorithm, allowing chatbots to learn and refine their responses based on positive or negative feedback from human interactions, thereby improving the efficiency and naturalness of human-chatbot conversations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If chatbots are trained using traditional machine learning methods on human-machine interactions, then the training data volume increases and training time extends, but the chatbot's ability to provide natural human-like conversations is limited
Solution Approach 1:
The system performs preliminary analysis of human-human conversations to extract actionable parameters, intents, and responses before actual chatbot training. By pre-processing and pre-analyzing human interaction patterns, the system prepares training data in advance, reducing the time and computational resources needed during the actual training phase while improving the naturalness of chatbot responses.
2Measurement precision
If every portion of human-machine dialogue is labeled and analyzed in detail, then the chatbot can learn from comprehensive data, but the process becomes extremely time-intensive and data-intensive
Solution Approach 1:
The system extracts only the most relevant and valuable elements from human-human conversations, such as actionable parameters, user intents, and effective responses, rather than labeling and analyzing every single dialogue component. This selective extraction approach maintains high training quality while dramatically reducing the time and computational resources required.
Solution Approach 2:
Instead of performing complete and exhaustive labeling of all dialogue portions, the system applies partial action by focusing on key conversational elements that have the greatest impact on chatbot performance. This partial analysis approach achieves sufficient training precision without the prohibitive time costs of comprehensive labeling.
3Ease of manufacture
If chatbots are trained solely on human-machine interactions, then the training process is streamlined, but the chatbot mimics machine-style responses rather than natural human conversations
Solution Approach 1:
The system uses human-human conversation analysis as an intermediary step between raw human interaction data and chatbot training. By analyzing how humans naturally communicate with each other and extracting patterns from these interactions, the system creates a bridge that enables chatbots to learn natural human conversation styles while maintaining training process efficiency.
Data Source
AI summary
A method for determining machine learning training parameters is disclosed. The method can include a processor receiving a first input. The processor may receive a first response to the first input, determine a first intent, and identify a first action. The processor can then determine first trainable parameter(s) and determine whether the first trainable parameter(s) is negative or positive. Further, the processor can update a training algorithm based on the first trainable parameter(s). The processor can then receive a second input and determine a second intent for the second input. The processor can also determine a second action for the second intent and transmit the second action to a user. The processor can then determine second trainable parameter(s) and determine whether the second trainable parameter(s) is positive or negative. Finally, the processor can further update the training algorithm based on the second trainable parameter(s).