NLU Training Data Refinement for Intent and Slot Ambiguity
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Solution Overview
Problem
Current development platforms for natural language understanding systems lack tools for refining intent and slot definitions, improving data organization, and supporting the selection of the best algorithm, leading to suboptimal performance as systems expand and training data becomes disorganized.
Innovation Solution
A development platform that includes tools for relabeling and deleting training sentences, merging similar slot-value lists and intents, reorganizing training sentences, and comparing different algorithms to optimize the natural language understanding model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If more training data is provided to expand the conversation system coverage, then the system can handle increased number of use cases, but the complexity of slot/intent information increases and ambiguities emerge making the training data disorganized
Solution Approach 1:
The patent segments the training data management process into distinct modules: data import module, analysis module, optimization module, and reorganization module. Each module handles specific tasks such as importing training data, analyzing ambiguities, optimizing slot/intent definitions, and reorganizing data structure. This segmentation allows the system to manage complex training data through systematic processing steps.
Solution Approach 2:
The patent introduces an intermediary processing system that acts as a mediator between the raw training data and the natural language understanding model. This intermediary system includes components for analyzing ambiguities, optimizing definitions, and reorganizing data, which transforms disorganized training data into structured, optimized data before model training, thereby reducing complexity while maintaining coverage.
2Ease of manufacture
If the same natural language understanding algorithm is used regardless of data characteristics, then the development process is simplified, but the algorithm may not be the best option for expanded conversation systems with different data characteristics
Solution Approach 1:
The patent implements a dynamic algorithm selection mechanism that adapts the natural language understanding algorithm based on the characteristics of the training data. The system analyzes data characteristics such as size, complexity, and ambiguity levels, then dynamically selects or configures the most appropriate algorithm, rather than using a fixed algorithm regardless of data properties.
Solution Approach 2:
The patent changes key parameters of the natural language understanding system based on data characteristics. This includes adjusting algorithm parameters, model complexity, and processing configurations according to the specific properties of the training data, such as the level of ambiguity, size of the dataset, and complexity of slot/intent structures.
3Adaptability or versatility
If training data is continuously expanded without reorganization, then more use cases are covered, but the training data becomes disorganized and performance deteriorates over time
Solution Approach 1:
The patent applies preliminary action by reorganizing and optimizing training data before each training cycle. The system proactively analyzes the training data for ambiguities, optimizes slot and intent definitions, and reorganizes the data structure in advance of model training, preventing data disorganization from accumulating and degrading performance.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors training data quality and model performance. Based on this feedback, the system automatically identifies ambiguities, optimizes definitions, and reorganizes data structures. This closed-loop feedback ensures that training data quality is maintained and improved over time as the system expands to cover more use cases.
Data Source
AI summary
A method for optimizing performance of a natural language understanding model is disclosed. The method comprises receiving a plurality of training sentences, each training sentence being labeled with a respective intent from a plurality of intents, at least one portion of at least some training sentences being labeled with a respective slot type from a plurality of slot types. The method comprises determining a recommended modification to the plurality of training sentences, the recommended modification including at least one of (i) relabeling and (ii) deleting a training sentence in the plurality of training sentences. The method comprises generating a modified plurality of training sentences by performing the recommended modification to the plurality of training sentences in response to a user input received via a user interface. The method comprises training a first natural language understanding model using the modified plurality of training sentences.