NLU Grammar Selection via Annotation Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Non-expert designers face challenges in selecting the best model for Natural Language Understanding (NLU) systems, as choosing the optimal model is complex and difficult, especially when comparing manually-generated and machine-generated annotations for intents and mentions.
Innovation Solution
A system and method for ranking models by comparing manually-generated and machine-generated annotations, applying scores based on weightings of intents and mentions, and optimizing user experience through statistical regression, to recommend the most effective grammar for NLU processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple grammars are generated for NLU processing, then the coverage and versatility of the system improve, but the complexity of selecting the best model increases
Solution Approach 1:
The system implements automated evaluation that provides feedback on grammar performance by comparing machine-generated annotations against manually-generated annotations. This feedback loop enables non-expert designers to objectively assess multiple grammars and select the best performing model without needing deep NLU expertise.
Solution Approach 2:
The patent introduces an intermediary evaluation system that acts as a mediator between multiple grammars and the final selection. This intermediary automatically scores and ranks grammars based on annotation accuracy, simplifying the selection process for non-expert users while maintaining high versatility in grammar coverage.
2Productivity
If automated annotation is used to generate models, then productivity increases, but measurement precision of annotation quality decreases
Solution Approach 1:
The system uses manually-generated annotations as a reference standard to provide feedback on the quality of machine-generated annotations. This feedback mechanism enables continuous measurement and improvement of annotation precision while maintaining high productivity through automated processing.
Solution Approach 2:
The patent employs statistical regression and scoring parameters to quantitatively measure annotation quality. By changing the measurement parameters to include both speed metrics and precision metrics, the system can simultaneously optimize for productivity while maintaining measurement precision through automated evaluation.
3Measurement precision
If manual analysis is used to select intents and mentions, then measurement precision of model quality improves, but loss of time increases
Solution Approach 1:
The system creates copies of manual annotation standards and applies them automatically to evaluate multiple grammars. By copying the manual evaluation criteria into an automated scoring system, the patent maintains measurement precision while eliminating the time loss associated with manual analysis of each grammar.
Solution Approach 2:
The patent replaces the mechanical process of manual model evaluation with an automated computational system. This substitution maintains the precision of expert-level evaluation while dramatically reducing the time required, as the automated system can evaluate multiple grammars simultaneously using statistical regression and scoring algorithms.
Data Source
AI summary
Selecting a grammar for use in a machine question-answering system, such as a Natural Language Understanding System, can be difficult for non-experts in such grammars. A tool, according to an example embodiment, can compare annotations of sample sentences, performed correctly by a human, the annotations having intents and mentions, against annotations performed by multiple grammars. Each grammar can be scored, and the system can select the best scored grammar for the user. In one embodiment, a method of selecting a grammar includes comparing manually-generated annotations against machine-generated annotations as a function of a given grammar among multiple grammars. The method can further include applying scores to the machine-generated annotations that are a function of weightings of the intents and mentions. The method can additionally include recommending whether to employ the given grammar based on the scores.