Intelligent Training Data Curation for Dialogue Systems
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Solution Overview
Problem
Modern virtual assistants implemented with a rules-based approach lack flexibility to handle unrecognized queries or commands, and existing machine learning models face inefficiencies in training, particularly in conversational systems with limited access to large volumes of training data.
Innovation Solution
A system and method utilizing deep machine learning models, such as LSTM neural networks, for an artificial intelligence virtual assistant platform that can process and comprehend natural language inputs, evolve with user interactions, and efficiently curate training data through intelligent sourcing and curation techniques, reducing the need for additional programming and minimizing sub-optimal data usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a rules-based approach is used to implement a virtual assistant, then the system can provide reliable responses to specific predefined queries, but the system lacks flexibility to handle unrecognized or varied user queries
Solution Approach 1:
The patent replaces the mechanical rules-based system with a machine learning model that processes natural language inputs. The model learns patterns from training data and generates responses without requiring explicit programming for each query type, thereby substituting rigid mechanical rule-following with adaptive intelligent processing.
Solution Approach 2:
The system changes the operational parameters from fixed rules to dynamic machine learning predictions. By training the model on diverse query examples, the system adapts its response behavior based on learned patterns rather than predetermined rules, enabling flexible handling of varied user inputs while maintaining reliability through consistent model inference.
2Measurement precision
If large volumes of training data are used to train machine learning models, then the model performance improves, but the training time and computational resources increase significantly
Solution Approach 1:
The patent extracts only the most relevant and informative features from training data using automated feature selection techniques. By identifying and retaining only the critical features that contribute to model performance, the system reduces the effective training data complexity and dimensionality, enabling faster training while maintaining or improving model accuracy.
Solution Approach 2:
The system performs preliminary data processing and feature extraction before actual model training. By pre-processing the training data to identify and prepare only the most useful features in advance, the system reduces the computational burden during the training phase, thereby decreasing training time while preserving the information necessary for high model performance.
3Reliability
If comprehensive training data is collected from multiple sources, then the model becomes more robust, but the data quality and relevance may deteriorate due to inclusion of sub-optimal data
Solution Approach 1:
The patent applies different quality assessment criteria to different data sources and regions of the training dataset. Rather than treating all data uniformly, the system evaluates and weights data quality locally based on source reliability, relevance to the specific task, and data characteristics, thereby incorporating diverse data while maintaining overall data quality standards.
Solution Approach 2:
The system implements feedback mechanisms during data collection and training that monitor data quality metrics and model performance. Based on this feedback, the system adjusts data selection criteria to exclude sub-optimal data that would harm model performance, thereby maintaining robustness through selective data incorporation rather than comprehensive but unfiltered data collection.
Data Source
AI summary
Systems and methods of intelligent formation and acquisition of machine learning training data for implementing an artificially intelligent dialogue system includes constructing a corpora of machine learning test corpus that comprise a plurality of historical queries and commands sampled from production logs of a deployed dialogue system; configuring training data sourcing parameters to source a corpora of raw machine learning training data from remote sources of machine learning training data; calculating efficacy metrics of the corpora of raw machine learning training data, wherein calculating the efficacy metrics includes calculating one or more of a coverage metric value and a diversity metric value of the corpora of raw machine learning training data; using the corpora of raw machine learning training data to train the at least one machine learning classifier if the calculated coverage metric value of the corpora of machine learning training data satisfies a minimum coverage metric threshold.