Generative Model Utterance Generation for Chatbot Training
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Solution Overview
Problem
Current methods for training conversational AI chatbots are resource-intensive and time-consuming, requiring large datasets of manually generated utterances, which leads to imbalances in training data distribution and prolonged training times, especially in domains like healthcare where some service codes have significantly fewer examples than others.
Innovation Solution
A system utilizing a generative machine learning model and a discriminative machine learning model, along with a reinforcement learning algorithm, to programmatically generate utterances that are contextually relevant and grammatically correct, reducing the need for manual intervention and addressing data imbalances by adjusting a randomness indicator based on grammar and relevance context scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If manual utterance gathering is used to train chatbots, then training data can be collected, but the process is resource-intensive and time-consuming
Solution Approach 1:
The system uses automated machine learning models (generative and discriminative) to generate and evaluate utterances without human intervention. The generative model creates utterance variants while the discriminative model filters them, forming a self-sufficient pipeline that eliminates manual gathering while producing millions of training examples efficiently
Solution Approach 2:
The system changes the parameter of randomness by adjusting the temperature parameter in the generative model. By dynamically modifying this parameter based on feedback from the discriminative model, the system optimizes the balance between generating diverse utterances and maintaining their quality, thereby increasing productivity without sacrificing training data quality
2Quantity of substance
If manual utterance gathering is used, then utterances can be collected, but data distribution becomes imbalanced across different service codes
Solution Approach 1:
The automated generation system naturally addresses distribution imbalances by generating utterances for all service codes proportionally. Unlike manual collection where experts may focus on more common or interesting cases, the systematic automated approach ensures uniform coverage across all service codes, creating balanced training data distribution
Solution Approach 2:
The system generates excessive numbers of utterances for underrepresented service codes by leveraging the generative model's ability to create multiple variants from limited seeds. This over-generation followed by discriminative filtering ensures that even rare service codes receive sufficient training examples, correcting the imbalances inherent in manual collection
3Quantity of substance
If large numbers of utterances are generated manually, then training data volume increases, but resource consumption and cost increase
Solution Approach 1:
The system performs partial generation by creating only the necessary number of utterance variants needed for balanced training data. Rather than generating all possible variants exhaustively, it uses the discriminative model to filter and select the most promising candidates, reducing unnecessary computational waste while still producing sufficient training data volume
Solution Approach 2:
The discriminative machine learning model provides feedback to the generative model by evaluating generated utterances and selecting those that meet quality criteria. This feedback mechanism creates an efficient pipeline where computational resources are focused on generating and refining only the most valuable utterances, rather than uniformly processing all possible variants, thereby reducing overall resource consumption
Data Source
AI summary
Methods, apparatus, systems, computing devices, computing entities, and/or the like for programmatically generating text strings are provided. An example method may include receiving an input string; generating a first utterance set based at least in part on the input string and a randomness indicator via a generative machine learning model; generating at least one grammar context score for the first utterance set; generating at least one relevance context score for the first utterance set; adjusting the randomness indicator based at least in part on one or more of the at least one grammar context score, a grammar context threshold, the at least one relevance context score, and a relevance context threshold; and generating a second utterance set based at least in part on the input string and the adjusted randomness indicator.