Summary-Grounded Conversation Generator for Training Data
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Solution Overview
Problem
The collection of human-to-human conversation logs for training conversational AI models is time-consuming, costly, and often yields questionable data quality, limiting the effectiveness of conversation summarization datasets.
Innovation Solution
A system that uses a trained summary-grounded conversation generator to produce diverse conversations from a given summary, which can then augment training datasets, improving the performance of downstream summarization models through automatic and human evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If human-to-human conversation logs are collected for training, then training data is obtained, but the process is time-consuming and costly
Solution Approach 1:
The patent uses a conversation generator to create synthetic conversation data that copies the structure and characteristics of real human conversations. This allows obtaining training data without the time-consuming process of collecting actual human-to-human conversation logs, while maintaining data quality suitable for training summarization models
2Quantity of substance
If human-to-human conversation logs are collected for training, then training data is obtained, but the cost increases
Solution Approach 1:
The conversation generator creates synthetic training data that replicates real conversation patterns without requiring expensive human annotators or data collection processes. This significantly reduces the cost of obtaining training data while maintaining sufficient quality for model training
Solution Approach 2:
The system uses pre-trained language models to generate conversation data autonomously without requiring human intervention for data collection or annotation. This self-service approach eliminates the need for costly human-to-human conversation logging while producing adequate training data
3Quantity of substance
If human-to-human conversation logs are used, then training data is obtained, but data quality is questionable
Solution Approach 1:
The patent generates synthetic conversation data that copies the structural and linguistic patterns of high-quality human conversations while avoiding the quality issues associated with collected data. The generated conversations maintain coherence, relevance, and natural flow, providing reliable training data for summarization models
Solution Approach 2:
The patent replaces the mechanical process of collecting and annotating human conversations with an automated AI-based generation system. This substitution eliminates quality control issues related to human annotator variability and ensures consistent, high-quality training data generation
Data Source
AI summary
An example system includes a processor to receive a summary of a conversation to be generated. The processor can input the summary into a trained summary-grounded conversation generator. The processor can receive a generated conversation from the trained summary-grounded conversation generator.