Rule-Based Q&A Extraction for Automated Chatbot Training
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Solution Overview
Problem
Training chatbots to interact effectively with humans is a time and resource-intensive task due to the need for large volumes of manually labeled training data, particularly from unstructured documents like PDFs, which delays deployment and is prone to human error.
Innovation Solution
Implementing rule-based techniques to automatically extract question-and-answer pairs from digital documents, generating training data sets that can be directly input to machine learning models, reducing the need for human intervention and enhancing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data parsing and labeling is used to extract training data from unstructured documents, then the chatbot can be trained with accurate labeled data, but the process is time-consuming and resource-intensive
Solution Approach 1:
The system enables self-service by automatically extracting and labeling training data from unstructured documents without human intervention. The processor autonomously parses documents, identifies Q&A pairs, and generates labeled training datasets, eliminating the need for manual data preparation while maintaining accuracy.
Solution Approach 2:
The patent replaces the mechanical manual process of data parsing and labeling with an automated computational system. The processor executes algorithms to perform document parsing, feature extraction, and data labeling, substituting human labor with machine-based automation to reduce time and resource consumption.
2Quantity of substance
If unstructured documents like PDFs are used as training data sources, then comprehensive training content is available, but the chatbot cannot directly process these formats
Solution Approach 1:
The system introduces an intermediary processing layer that converts unstructured PDF documents into structured training data formats. The processor acts as a mediator by parsing the unstructured content, extracting relevant Q&A pairs, and transforming them into a format compatible with chatbot training requirements, enabling the system to utilize comprehensive document sources.
Solution Approach 2:
The patent applies parameter changes by transforming the structural parameters of training data from unstructured to structured formats. The system modifies the data organization, extracting specific features (questions and answers) from unstructured documents and reorganizing them into labeled training datasets with defined parameters that the chatbot can process effectively.
3Adaptability or versatility
If existing FAQ documents are used for chatbot training, then relevant training content is available, but manual parsing and format conversion are required
Solution Approach 1:
The system achieves self-service by automatically processing FAQ documents end-to-end. The processor autonomously parses the documents, identifies relevant Q&A pairs, extracts training features, and generates labeled datasets without requiring manual intervention, thereby maintaining adaptability to relevant content while maximizing automation.
Solution Approach 2:
The patent applies extraction by selectively removing and isolating the essential training elements (questions and answers) from the FAQ documents. The system extracts only the relevant Q&A pairs and their corresponding features, separating them from the rest of the document content to create focused training datasets that maintain relevance while enabling automated processing.
Data Source
AI summary
Techniques are disclosed for rules-based techniques for extraction of question-and-answer pairs from digital documents. In an exemplary technique, a digital text document can be accessed by executing a document indicator. A document hierarchy can be generated. The document hierarchy can include at least one parent node and child nodes corresponding to the parent node. Each node of the child nodes can correspond to a text feature of the digital text document. At least a first child node of the child nodes can be determined that corresponds to a first text feature. The parent node of the first child node and a second child node of the parent node can be determined. The second child node can correspond to a second text feature related to the first text feature. A training data set can be generated.