Document-Based Response Generation System for Chatbot Training
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Solution Overview
Problem
Current chatbots require extensive manual effort to create and maintain a large dataset of user utterances and responses, which is labor-intensive and time-consuming, and may fail to provide meaningful responses to user queries if not adequately trained.
Innovation Solution
A system that autonomously generates utterances and answers based on policy documents using machine learning, reducing the need for manual intervention by filtering and scoring user-provided utterances to select relevant responses for output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual creation of training data is used, then chatbot can be trained with sufficient utterances and responses, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The system enables self-service by automatically generating training utterances and responses using domain documents. The utterance generator creates diverse user queries by analyzing domain content, and the response generator automatically formulates accurate answers, eliminating the need for manual data creation while ensuring high-quality training data for reliable chatbot responses.
Solution Approach 2:
The system performs preliminary action by pre-generating comprehensive training data before the chatbot deployment. By analyzing domain documents in advance and creating extensive utterance-response pairs, the system prepares sufficient training material that ensures the chatbot can handle various user queries reliably without requiring time-consuming manual preparation during deployment.
2Adaptability or versatility
If manual entry of all possible questions and responses is performed, then chatbot can provide meaningful responses to user queries, but the process becomes tedious and requires review of large quantities of policy documents
Solution Approach 1:
The system implements self-service by automatically generating diverse training utterances and responses from domain documents without manual intervention. The utterance generator extracts key information and creates multiple query variations, while the response generator formulates accurate answers, making the training data creation process easy and automated while ensuring the chatbot can handle various user queries adaptably.
Solution Approach 2:
The system applies universality by creating a multi-functional training data generation process that handles diverse domain documents (policy documents, manuals, etc.) and generates comprehensive utterance-response pairs covering various query types. This universal approach ensures the chatbot can adapt to different domains and query styles while simplifying the training data creation process through automated document analysis.
3Quantity of substance
If extensive manual review of policy documents is conducted, then sufficient training data can be created, but the process becomes labor-intensive
Solution Approach 1:
The system achieves self-service by automatically analyzing domain documents and generating extensive training data without manual review. The utterance generator extracts information and creates multiple query variations, while the response generator formulates comprehensive answers, producing large volumes of training data efficiently through automated document processing rather than labor-intensive manual review.
Solution Approach 2:
The system applies parameter changes by transforming the training data creation process from manual document review to automated text analysis. By changing the methodology parameter from human review to machine learning-based generation, the system produces large volumes of training data with high productivity, generating numerous utterance-response pairs efficiently from domain documents.
Data Source
AI summary
Examples provide a system for generating document-based responses to user provided queries. The response generation component creates a set of generated utterances based on text associated with a set of sentences in at least one document. Each utterance in the set of generated utterances is assigned an intent. A filter component identifies at least one utterance from the set of generated utterances having a same intent as a user-provided utterance to form a set of filtered utterances. A selection component identifies one or more utterance(s) from the set of filtered utterances having a shortest distance from the user-provided utterance. If more than one utterance is identified, a weighted summarization response is output to the user based on a predefined answer to each utterance in the selected set of utterances.