Chatbot Explanation System for Text Classification Interpretability
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Solution Overview
Problem
Chatbot systems lack interpretability and explainability, making it difficult for users to understand the behavior of machine learning models and trust their predictions, and there is no comprehensive way to integrate inference explanation systems into retraining processes to improve model performance.
Innovation Solution
A computer-implemented method that generates explanations for chatbot classifications by selecting anchor words from user utterances, varying non-anchor words to create synthetic utterances, and determining confidence levels based on inference results, allowing for the identification of trustworthy classifications and improvements to training data and models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used for text classification in chatbots, then classification accuracy and automation are improved, but interpretability and explainability deteriorate
Solution Approach 1:
The patent introduces an explanation generation system as an intermediary component between the machine learning classifier and the user. This system takes the classifier's output and generates human-readable explanations that describe why certain classifications were made, thereby mediating between the automated classification process and user understanding without requiring changes to the underlying ML model
Solution Approach 2:
The patent replaces the need for users to directly interpret complex machine learning model operations with a natural language explanation system. Instead of users attempting to understand the mechanical/internal workings of the ML model, the system substitutes this with generated explanations in natural language that convey the classification rationale in an accessible manner
2Measurement precision
If comprehensive training data is collected manually, then model accuracy is improved, but time and resource consumption increase
Solution Approach 1:
The patent enables the system to automatically generate synthetic training data and explanations without requiring extensive manual data collection and annotation. The explanation generation system serves itself by utilizing the classifier's existing outputs to create explanatory content, which can then be used to iteratively improve the model with minimal human intervention
Solution Approach 2:
The patent performs preliminary analysis and explanation generation during the classification process itself, rather than requiring separate manual data preparation steps. By generating explanations and identifying training needs as part of the ongoing classification operations, the system prepares training materials in advance of actual model retraining cycles
3Reliability
If model retraining is performed frequently, then model performance is improved, but computational cost and time increase
Solution Approach 1:
The patent implements a feedback mechanism where the explanation generation system analyzes classification outcomes and identifies cases where explanations indicate potential model weaknesses or errors. This feedback is used to selectively trigger retraining only for specific model components or with targeted training data, rather than performing comprehensive retraining of the entire model
Solution Approach 2:
The patent applies partial retraining strategies by focusing computational resources only on the specific parts of the model that the explanation system identifies as needing improvement. Rather than retraining the entire model frequently, the system performs targeted updates on specific classifiers or parameters that show performance degradation
Data Source
AI summary
A chatbot system is configured to execute code to perform determining, by the chatbot system, a classification result for an utterance and one or more anchors each anchor of the one or more anchors corresponding to one or more anchor words of the utterance. For each anchor of the one or more anchors, one or more synthetic utterances are generated, and one or more classification results for the one or more synthetic utterances are determined. A report is generated by the chatbot system including a representation of a particular anchor of the one or more anchors, the particular anchor corresponding to a highest confidence value among the one or more anchors. The one or more synthetic utterances may be used to generate a new training dataset for training a machine-learning model. The training dataset may be refined according to a threshold confidence values to filter out datasets for training.